knitr::opts_chunk$set(
warning = TRUE, # show warnings during codebook generation
message = TRUE, # show messages during codebook generation
error = TRUE, # do not interrupt codebook generation in case of errors,
# usually better for debugging
echo = TRUE # show R code
)
ggplot2::theme_set(ggplot2::theme_bw())
pander::panderOptions("table.split.table", Inf)
library(codebook)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
#codebook_data <- codebook::bfi
# to import an SPSS file from the same folder uncomment and edit the line below
# codebook_data <- rio::import("mydata.sav")
# for Stata
# codebook_data <- rio::import("mydata.dta")
# for CSV
codebook_data <- rio::import("online_data_non_shared_condition.csv")
#drop <- c("V1", "submitdate", "startlanguage", "click")
#codebook_data = codebook_data[ , !(names(codebook_data) %in% drop)]
# omit the following lines, if your missing values are already properly labelled
codebook_data <- detect_missing(codebook_data,
only_labelled = TRUE, # only labelled values are autodetected as
# missing
negative_values_are_missing = FALSE, # negative values are missing values
ninety_nine_problems = TRUE # 99/999 are missing values, if they
# are more than 5 MAD from the median
)
# If you are not using formr, the codebook package needs to guess which items
# form a scale. The following line finds item aggregates with names like this:
# scale = scale_1 + scale_2R + scale_3R
# identifying these aggregates allows the codebook function to
# automatically compute reliabilities.
# However, it will not reverse items automatically.
#codebook_data <- detect_scales(codebook_data)
var_label(codebook_data) <- list(
id = "ID variable from raw data",
lastpage = "Last page completed by the participant, page 12 and 13 are considered as full participation",
random = "Randomly attributed study condition. 1 & 2 = not shared, 3 & 4 = shared, 1 & 3 = high anchor in anchoring paradigm, 2 & 4 = low anchor.",
cb = "No data sharing policy consent presented. One participant clicked on 'I disagree' but contacted the first author by email to indicate that they had 'a bug' and was unable to complete the questionnaire. See manuscript for details",
ca = "Data sharing policy presented",
mc_1 = "comprehension question consent 1 (distractor): Will this survey take longer than 10 minutes?",
mc_2 = "comprehension question consent 2 (distractor): Is your data anonymous?",
mc_3 = "comprehension question/manipulation check: will your data be shared? correct answer depends on condition: Will your data be shared?",
mc_4 = "comprehension question consent 3 (distractor): Can you stop your participation at any time?",
bf_1 = "TIPI item 1, Extraversion: I see myself as extraverted, enthousiastic.",
bf_2 = "TIPI item 2, Agreeableness: I see myself as critical, quarrelsome.",
bf_3 = "TIPI item 3, Conscientiousness: I see myself as dependable, self-disciplined.",
cr_1 = "Careless response item 1: I am using an electronic device at this moment.",
bf_5 = "TIPI item 4, Neuroticsm: I see myself as anxious, easily upset.",
bf_6 = "TIPI item 5, Openness to experience: I see myself as open to new experiences, complex.",
bf_7 = "TIPI item 6, Extraversion: I see myself as reserved, quiet.",
bf_8 = "TIPI item 7, Agreeableness: I see myself as sympathetic, warm.",
cr_2 = "Careless response item 2: I turn into a leprechaun at night.",
bf_10 = "TIPI item 8, Conscientiousness: I see myself as disorganized, careless.",
bf_11 = "TIPI item 9, Neuroticsm: I see myself as calm, emotionally stable.",
bf_12 = "TIPI item 10, Openness to experience: I see myself as conventional, uncreative.",
soc_d_1 = "Social desirability questionnaire item 1: Before voting I thoroughly investigate the qualifications of all the candidates.",
soc_d_2 = "Social desirability questionnaire item 2: I never hesitate to go out of my way to help someone in trouble.",
soc_d_3 = "Social desirability questionnaire item 3: It is sometimes hard for me to go on with my work if I am not encouraged.",
soc_d_4 = "Social desirability questionnaire item 4: I have never intensely disliked anyone.",
soc_d_5 = "Social desirability questionnaire item 5: On occasion I have had doubts about my ability to succeed in life.",
soc_d_6 = "Social desirability questionnaire item 6: I sometimes feel resentful when I don't get my way.",
cr_3 = "Careless response item 3: All my friends are aliens.",
soc_d_7 = "Social desirability questionnaire item 7: I am always careful about my manner of dress.",
soc_d_8 = "Social desirability questionnaire item 8: My table manners at home are as good as when I eat out in a restaurant.",
soc_d_9 = "Social desirability questionnaire item 9: If I could get into a movie without paying and be sure I was not seen I would probably do it.",
soc_d_10 = "Social desirability questionnaire item 10: On a few occasions, I have given up doing something because I thought too little of my ability.",
soc_d_11 = "Social desirability questionnaire item 11: I like to gossip at times.",
soc_d_12 = "Social desirability questionnaire item 12: There have been times when I felt like rebelling against people in authority even though I knew they were right.",
cr_4 = "Careless response item 4: All my friends say I would make a great poodle.",
soc_d_13 = "Social desirability questionnaire item 13: No matter who I'm talking to, I'm always a good listener.",
soc_d_14 = "Social desirability questionnaire item 14: I can remember 'playing sick' to get out of something.",
soc_d_15 = "Social desirability questionnaire item 15: There have been occasions when I took advantage of someone.",
soc_d_16 = "Social desirability questionnaire item 16: I'm always willing to admit it when I make a mistake.",
soc_d_17 = "Social desirability questionnaire item 17: I always try to practice what I preach.",
soc_d_18 = "Social desirability questionnaire item 18: I don't find it particularly difficult to get along with loud mouthed, obnoxious people.",
cr_5 = "Careless response item 5: I eat cement occasionally.",
soc_d_19 = "Social desirability questionnaire item 19: I sometimes try to get even rather than forgive and forget.",
soc_d_20 = "Social desirability questionnaire item 20: When I don't know something I don't at all mind admitting it.",
soc_d_21 = "Social desirability questionnaire item 21: I am always courteous, even to people who are disagreeable.",
soc_d_22 = "Social desirability questionnaire item 22: At times I have really insisted on having things my own way.",
soc_d_23 = "Social desirability questionnaire item 23: There have been occasions when I felt like smashing things.",
soc_d_24 = "Social desirability questionnaire item 24: I would never think of letting someone else be punished for my wrong- doings.",
soc_d_25 = "Social desirability questionnaire item 25: I never resent being asked to return a favor.",
soc_d_26 = "Social desirability questionnaire item 26: I have never been irked when people expressed ideas very different from my own.",
cr_6 = "Careless response item 6: I can teleport across time and space.",
soc_d_27 = "Social desirability questionnaire item 27: I never make a long trip without checking the safety of my car.",
soc_d_28 = "Social desirability questionnaire item 28: There have been times when I was quite jealous of the good fortune of others.",
soc_d_29 = "Social desirability questionnaire item 29: I have almost never felt the urge to tell someone off.",
soc_d_30 = "Social desirability questionnaire item 30: I am sometimes irritated by people who ask favors of me. ",
soc_d_31 = "Social desirability questionnaire item 31: I have never felt that I was punished without cause.",
cr_7 = "Careless response item 7: I will be punished for meeting the requirements of my job.",
soc_d_32 = "Social desirability questionnaire item 32: I sometimes think when people have a misfortune they only got what they deserved.",
soc_d_33 = "Social desirability questionnaire item 33: I have never deliberately said something that hurt someone's feelings.",
everesthigh = "Anchoring paradigm, high anchor: Height of Mount Everest",
chicagohigh = "Anchoring paradigm, high anchor: Population of Chicago",
babieshigh = "Anchoring paradigm, high anchor: Babies born each day",
everestlow = "Anchoring paradigm, low anchor: Height of Mount Everest",
chicagolow = "Anchoring paradigm, low anchor: Population of Chicago",
babieslow = "Anchoring paradgim, low anchor: Babies born each day",
d1 = "NOT USED control question: memory of consent, not used: Think back to the beginning of this study. Do you remember clicking through a consent form, and the information it contained?",
d2.sq001 = "NOT USED Answer option to control question d2: 'Do you remember if the consent form dealt with making your anonymous data accessible to others on osf.io?' : Yes, I remember",
d2.sq002 = "NOT USED Answer option to control question d2: 'Do you remember if the consent form dealt with making your anonymous data accessible to others on osf.io?' : No, I don't remember",
d3.sq001 = "NOT USED Answer option to control question d2: 'Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?': Yes
",
d3.sq002 = "NOT USED Answer option to control question d2: 'Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?': No",
d3.sq003 = "NOT USED Answer option to control question d2: 'Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?': I don't remember",
gender = "Gender: open-entry self-report",
age = "Age categories",
consent = "Variables cb and ca combined in one variable",
cond_anc = "Anchoring condition: high and low",
refused = "Refusal to participate, one participant clicked on 'I disagree' but contacted the first author by email to indicate that they had 'a bug' and was unable to complete the questionnaire. This participant was in the 'no data sharing' condition.",
remember = "Mutated variable from consent and mc_3: Does the participant remember the correct data sharing policy?",
anc_baby = "Aggregated anchoring response, combining variables babieshigh and babieslow in one variable",
anc_everest = "Aggregated anchoring response, combining variables everesthigh and everestlow in one variable",
anc_chicago = "Aggregated anchoring response, combining variables chicagohigh and chicagolow in one variable",
gender_r = "Gender variable cleaned for grammar, language variations and orthography"
)
val_labels(codebook_data$remember) <- c("No or wrong memory" = 0, "correct memory" = 1)
add_likert_labels <- function(x) {
val_labels(x) <- c("No" = 0,
"Yes" = 1)
x
}
likert_items <- names(codebook_data[, c("soc_d_1", "soc_d_2", "soc_d_3", "soc_d_4", "soc_d_5", "soc_d_6", "soc_d_7", "soc_d_8", "soc_d_9", "soc_d_10", "soc_d_11", "soc_d_12", "soc_d_13", "soc_d_14", "soc_d_15", "soc_d_16", "soc_d_17", "soc_d_18", "soc_d_19", "soc_d_20", "soc_d_21", "soc_d_22", "soc_d_23", "soc_d_24", "soc_d_25", "soc_d_26", "soc_d_27", "soc_d_28", "soc_d_29", "soc_d_30", "soc_d_31", "soc_d_32", "soc_d_33") ])
codebook_data <- codebook_data %>% mutate_at(likert_items, add_likert_labels)
add_likert_labels <- function(x) {
val_labels(x) <- c("Yes" = 0,
"No" = 1)
x
}
likert_items <- names(codebook_data[, c("cr_1", "cr_2", "cr_3", "cr_4", "cr_5", "cr_6", "cr_7") ])
codebook_data <- codebook_data %>% mutate_at(likert_items, add_likert_labels)
add_likert_labels <- function(x) {
val_labels(x) <- c("Disagree strongly" = 1,
"Disagree moderately" = 2,
"Disagree a little" = 3,
"Neither agree nor disagree" = 4,
"Agree a little" = 5,
"Agree moderately" = 6,
"Agree strongly" = 7)
x
}
likert_items <- names(codebook_data[, c("bf_1", "bf_2",
"bf_3", "bf_5", "bf_6",
"bf_7", "bf_8", "bf_10",
"bf_11", "bf_12") ])
codebook_data <- codebook_data %>% mutate_at(likert_items, add_likert_labels)
#### Extraversion ####
codebook_data$Extraversion <- codebook_data %>% select("bf_1", "bf_7") %>% aggregate_and_document_scale()
reversed_items <- c("bf_7")
codebook_data <- codebook_data %>%
rename_at(reversed_items, add_R)
codebook_data <- codebook_data %>%
mutate_at(vars(matches("\\dR$")), reverse_labelled_values)
codebook_data$Extraversion <- codebook_data %>% select("bf_1", "bf_7R") %>% aggregate_and_document_scale()
#### Agreeableness ####
codebook_data$Agreeableness <- codebook_data %>% select("bf_2", "bf_8") %>% aggregate_and_document_scale()
reversed_items <- c("bf_2")
codebook_data <- codebook_data %>%
rename_at(reversed_items, add_R)
codebook_data <- codebook_data %>%
mutate_at(vars(matches("\\dR$")), reverse_labelled_values)
codebook_data$Agreeableness <- codebook_data %>% select("bf_2R", "bf_8") %>% aggregate_and_document_scale()
#### Conscientiousness ####
codebook_data$Conscientiousness <- codebook_data %>% select("bf_3", "bf_10") %>% aggregate_and_document_scale()
reversed_items <- c("bf_10")
codebook_data <- codebook_data %>%
rename_at(reversed_items, add_R)
codebook_data <- codebook_data %>%
mutate_at(vars(matches("\\dR$")), reverse_labelled_values)
codebook_data$Conscientiousness <- codebook_data %>% select("bf_3", "bf_10R") %>% aggregate_and_document_scale()
#### Neuroticism ####
codebook_data$Neuroticism <- codebook_data %>% select("bf_5", "bf_11") %>% aggregate_and_document_scale()
reversed_items <- c("bf_5")
codebook_data <- codebook_data %>%
rename_at(reversed_items, add_R)
codebook_data <- codebook_data %>%
mutate_at(vars(matches("\\dR$")), reverse_labelled_values)
codebook_data$Neuroticism <- codebook_data %>% select("bf_5R", "bf_11") %>% aggregate_and_document_scale()
#### Openness to experience ####
codebook_data$'Openness to experience' <- codebook_data %>% select("bf_6", "bf_12") %>% aggregate_and_document_scale()
reversed_items <- c("bf_12")
codebook_data <- codebook_data %>%
rename_at(reversed_items, add_R)
codebook_data <- codebook_data %>%
mutate_at(vars(matches("\\dR$")), reverse_labelled_values)
codebook_data$'Openness to experience' <- codebook_data %>% select("bf_6", "bf_12R") %>% aggregate_and_document_scale()
#### Social Desirability ####
codebook_data$'Social Desirability' <- codebook_data %>% select("soc_d_1", "soc_d_2", "soc_d_3",
"soc_d_4", "soc_d_5", "soc_d_6",
"soc_d_7", "soc_d_8", "soc_d_9",
"soc_d_10", "soc_d_11", "soc_d_12",
"soc_d_13", "soc_d_14", "soc_d_15",
"soc_d_16", "soc_d_17", "soc_d_18",
"soc_d_19", "soc_d_20", "soc_d_21",
"soc_d_22", "soc_d_23", "soc_d_24",
"soc_d_25", "soc_d_26", "soc_d_27",
"soc_d_28", "soc_d_29", "soc_d_30",
"soc_d_31", "soc_d_32", "soc_d_33") %>% aggregate_and_document_scale()
reversed_items <- c("soc_d_3", "soc_d_5", "soc_d_6", "soc_d_9",
"soc_d_10", "soc_d_11","soc_d_12","soc_d_14",
"soc_d_15", "soc_d_19", "soc_d_22", "soc_d_23",
"soc_d_28", "soc_d_30", "soc_d_32"
)
codebook_data <- codebook_data %>%
rename_at(reversed_items, add_R)
codebook_data <- codebook_data %>%
mutate_at(vars(matches("\\dR$")), reverse_labelled_values)
codebook_data$'Social Desirability' <- codebook_data %>% select("soc_d_1", "soc_d_2", "soc_d_3R",
"soc_d_4", "soc_d_5R", "soc_d_6R",
"soc_d_7", "soc_d_8", "soc_d_9R",
"soc_d_10R", "soc_d_11R", "soc_d_12R",
"soc_d_13", "soc_d_14R", "soc_d_15R",
"soc_d_16", "soc_d_17", "soc_d_18",
"soc_d_19R", "soc_d_20", "soc_d_21",
"soc_d_22R", "soc_d_23R", "soc_d_24",
"soc_d_25", "soc_d_26", "soc_d_27",
"soc_d_28R", "soc_d_29", "soc_d_30R",
"soc_d_31", "soc_d_32R", "soc_d_33") %>% aggregate_and_document_scale()
codebook_data$'Careless responses' <- codebook_data %>% select("cr_1", "cr_2", "cr_3",
"cr_4", "cr_5", "cr_6",
"cr_7") %>% aggregate_and_document_scale()
reversed_items <- c("cr_1")
codebook_data <- codebook_data %>%
rename_at(reversed_items, add_R)
codebook_data <- codebook_data %>%
mutate_at(vars(matches("\\dR$")), reverse_labelled_values)
codebook_data$'Careless responses' <- codebook_data %>% select("cr_1R", "cr_2", "cr_3",
"cr_4", "cr_5", "cr_6",
"cr_7") %>% aggregate_and_document_scale()
metadata(codebook_data)$name <- "Online (Prolific.co) data on TIPI, Social Desirability, Careless Response and Anchoring Paradigm, confidential data set"
metadata(codebook_data)$description <- "10 items taking from the Very brief measure of the Big 5 Personality questionnaire (Gosling, Rentfrow, & Swann, 2003), 33 items from the Social desirability scale (Crowne & Marlowe, 1960) and 3 Anchoring paradigm items as used in the ManyLabs replication project (Klein et al., 2014). Also includes 7 careless response items based on Meade and Craig (2012). This dataset cannot be publicly shared, as the study consent for the participants represented in this data set stated that we would not provide public access to the data. If you are interested in re-analyzing the entire dataset, please contact the authors. Please find the shareable half of the data set on our osf.io page (see doi)"
metadata(codebook_data)$identifier <- "https://doi.org/10.17605/OSF.IO/AM6BC"
metadata(codebook_data)$creator <- "Julia C. Eberlen, Emmanuel Nicaise, Sarah Leveaux, Youri L. Mora, Olivier Klein"
metadata(codebook_data)$citation <- "Eberlen, J. C., Nicaise, E., Leveaux, S., Mora, Y., & Klein, O. (2019, August 5). Data collected online. https://doi.org/10.17605/OSF.IO/AM6BC"
metadata(codebook_data)$datePublished <- "2019-08-06"
metadata(codebook_data)$temporalCoverage <- "2019-06-17 to 2019-06-21"
metadata(codebook_data)$spatialCoverage <- "Online participants residing in, or citizens of, the EU at time of data collection"
#rio::export(codebook_data, "offline_data_shared.rds")
codebook(codebook_data)
## Warning in doTryCatch(return(expr), name, parentenv, handler): Reliability
## CIs could not be computed for Careless responses
## Warning in doTryCatch(return(expr), name, parentenv, handler): missing
## value where TRUE/FALSE needed
knitr::asis_output(data_info)
if (exists("name", meta)) {
glue::glue(
"__Dataset name__: {name}",
.envir = meta)
}
Dataset name: Online (Prolific.co) data on TIPI, Social Desirability, Careless Response and Anchoring Paradigm, confidential data set
cat(description)
10 items taking from the Very brief measure of the Big 5 Personality questionnaire (Gosling, Rentfrow, & Swann, 2003), 33 items from the Social desirability scale (Crowne & Marlowe, 1960) and 3 Anchoring paradigm items as used in the ManyLabs replication project (Klein et al., 2014). Also includes 7 careless response items based on Meade and Craig (2012). This dataset cannot be publicly shared, as the study consent for the participants represented in this data set stated that we would not provide public access to the data. If you are interested in re-analyzing the entire dataset, please contact the authors. Please find the shareable half of the data set on our osf.io page (see doi)
Metadata for search engines
Citation: Eberlen, J. C., Nicaise, E., Leveaux, S., Mora, Y., & Klein, O. (2019, August 5). Data collected online. https://doi.org/10.17605/OSF.IO/AM6BC
Date published: 2019-08-06
Creator:Julia C. Eberlen, Emmanuel Nicaise, Sarah Leveaux, Youri L. Mora, Olivier Klein
meta <- meta[setdiff(names(meta),
c("creator", "datePublished", "identifier",
"url", "citation", "spatialCoverage",
"temporalCoverage", "description", "name"))]
pander::pander(meta)
knitr::asis_output(survey_overview)
if (detailed_variables || detailed_scales) {
knitr::asis_output(paste0(scales_items, sep = "\n\n\n", collapse = "\n\n\n"))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| V1 | integer | 0 | 263 | 263 | 297.31 | 171.15 | 1 | 149.5 | 300 | 445 | 576 | ▇▆▇▆▇▇▇▇ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
ID variable from raw data
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | label | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ID variable from raw data | integer | 0 | 263 | 263 | 328.89 | 192.86 | 1 | 161.5 | 330 | 495 | 654 | ▇▇▇▇▇▇▇▇ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
Variables cb and ca combined in one variable
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | label | data_type | missing | complete | n | empty | n_unique | min | max |
|---|---|---|---|---|---|---|---|---|---|
| consent | Variables cb and ca combined in one variable | character | 0 | 263 | 263 | 0 | 1 | 1 | 1 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
Anchoring condition: high and low
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | label | data_type | missing | complete | n | empty | n_unique | min | max |
|---|---|---|---|---|---|---|---|---|---|
| cond_anc | Anchoring condition: high and low | character | 0 | 263 | 263 | 0 | 2 | 1 | 1 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
Refusal to participate, one participant clicked on ‘I disagree’ but contacted the first author by email to indicate that they had ‘a bug’ and was unable to complete the questionnaire. This participant was in the ‘no data sharing’ condition.
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | label | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| refused | Refusal to participate, one participant clicked on ‘I disagree’ but contacted the first author by email to indicate that they had ‘a bug’ and was unable to complete the questionnaire. This participant was in the ‘no data sharing’ condition. | integer | 0 | 263 | 263 | 0.0038 | 0.062 | 0 | 0 | 0 | 0 | 1 | ▇▁▁▁▁▁▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
Mutated variable from consent and mc_3: Does the participant remember the correct data sharing policy?
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
1 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | label | data_type | value_labels | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| remember | Mutated variable from consent and mc_3: Does the participant remember the correct data sharing policy? | integer | 0. No or wrong memory, 1. correct memory |
1 | 262 | 263 | 0.73 | 0.44 | 0 | 0 | 1 | 1 | 1 | ▃▁▁▁▁▁▁▇ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
Aggregated anchoring response, combining variables babieshigh and babieslow in one variable
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
1 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | label | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| anc_baby | Aggregated anchoring response, combining variables babieshigh and babieslow in one variable | numeric | 1 | 262 | 263 | 77613.63 | 4e+05 | 1.8 | 1000 | 10000 | 36750 | 4e+06 | ▇▁▁▁▁▁▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
Aggregated anchoring response, combining variables everesthigh and everestlow in one variable
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
1 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | label | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| anc_everest | Aggregated anchoring response, combining variables everesthigh and everestlow in one variable | numeric | 1 | 262 | 263 | 8981.49 | 8176.08 | 8 | 6967 | 8500 | 9000 | 84000 | ▇▁▁▁▁▁▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
Aggregated anchoring response, combining variables chicagohigh and chicagolow in one variable
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
1 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | label | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| anc_chicago | Aggregated anchoring response, combining variables chicagohigh and chicagolow in one variable | numeric | 1 | 262 | 263 | 4.52 | 6.27 | 1e-04 | 2 | 3 | 5 | 50 | ▇▁▁▁▁▁▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
Gender variable cleaned for grammar, language variations and orthography
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
3 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | label | data_type | missing | complete | n | empty | n_unique | min | max |
|---|---|---|---|---|---|---|---|---|---|
| gender_r | Gender variable cleaned for grammar, language variations and orthography | character | 3 | 260 | 263 | 0 | 3 | 4 | 10 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
33 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | empty | n_unique | min | max |
|---|---|---|---|---|---|---|---|---|
| oq | character | 33 | 230 | 263 | 0 | 227 | 2 | 897 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
Anchoring paradigm, high anchor: Height of Mount Everest
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
137 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | label | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| everesthigh | Anchoring paradigm, high anchor: Height of Mount Everest | numeric | 137 | 126 | 263 | 11573.93 | 9024.16 | 8.85 | 8650 | 8900 | 13337.5 | 84000 | ▇▃▁▁▁▁▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
Anchoring paradigm, high anchor: Population of Chicago
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
137 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | label | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| chicagohigh | Anchoring paradigm, high anchor: Population of Chicago | numeric | 137 | 126 | 263 | 313337.14 | 1153850.61 | 1.3 | 3 | 3 | 7 | 7e+06 | ▇▁▁▁▁▁▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
Anchoring paradigm, high anchor: Babies born each day
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
137 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | label | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| babieshigh | Anchoring paradigm, high anchor: Babies born each day | numeric | 137 | 126 | 263 | 110755.6 | 455097.97 | 1.8 | 10000 | 30000 | 50000 | 4e+06 | ▇▁▁▁▁▁▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
Anchoring paradigm, low anchor: Height of Mount Everest
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
127 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | label | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| everestlow | Anchoring paradigm, low anchor: Height of Mount Everest | numeric | 127 | 136 | 263 | 6579.68 | 6461.63 | 8 | 2000 | 8000 | 8533 | 60000 | ▇▇▁▁▁▁▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
Anchoring paradigm, low anchor: Population of Chicago
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
127 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | label | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| chicagolow | Anchoring paradigm, low anchor: Population of Chicago | numeric | 127 | 136 | 263 | 409740.82 | 4297754.77 | 0.1 | 1.2 | 2.6 | 5.05 | 5e+07 | ▇▁▁▁▁▁▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
Anchoring paradgim, low anchor: Babies born each day
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
127 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | label | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| babieslow | Anchoring paradgim, low anchor: Babies born each day | integer | 127 | 136 | 263 | 46908.57 | 341493.94 | 10 | 230 | 1000 | 10000 | 3853000 | ▇▁▁▁▁▁▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
NOT USED control question: memory of consent, not used: Think back to the beginning of this study. Do you remember clicking through a consent form, and the information it contained?
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | label | data_type | missing | complete | n | empty | n_unique | min | max |
|---|---|---|---|---|---|---|---|---|---|
| d1 | NOT USED control question: memory of consent, not used: Think back to the beginning of this study. Do you remember clicking through a consent form, and the information it contained? | character | 0 | 263 | 263 | 0 | 3 | 2 | 3 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
NOT USED Answer option to control question d2: ‘Do you remember if the consent form dealt with making your anonymous data accessible to others on osf.io?’ : Yes, I remember
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | label | data_type | missing | complete | n | empty | n_unique | min | max |
|---|---|---|---|---|---|---|---|---|---|
| d2.sq001 | NOT USED Answer option to control question d2: ‘Do you remember if the consent form dealt with making your anonymous data accessible to others on osf.io?’ : Yes, I remember | character | 0 | 263 | 263 | 0 | 3 | 2 | 3 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
NOT USED Answer option to control question d2: ‘Do you remember if the consent form dealt with making your anonymous data accessible to others on osf.io?’ : No, I don’t remember
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | label | data_type | missing | complete | n | empty | n_unique | min | max |
|---|---|---|---|---|---|---|---|---|---|
| d2.sq002 | NOT USED Answer option to control question d2: ‘Do you remember if the consent form dealt with making your anonymous data accessible to others on osf.io?’ : No, I don’t remember | character | 0 | 263 | 263 | 0 | 3 | 2 | 3 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
NOT USED Answer option to control question d2: ‘Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?’: Yes
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | label | data_type | missing | complete | n | empty | n_unique | min | max |
|---|---|---|---|---|---|---|---|---|---|
| d3.sq001 | NOT USED Answer option to control question d2: ‘Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?’: Yes |
character | 0 | 263 | 263 | 0 | 3 | 2 | 3 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
NOT USED Answer option to control question d2: ‘Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?’: No
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | label | data_type | missing | complete | n | empty | n_unique | min | max |
|---|---|---|---|---|---|---|---|---|---|
| d3.sq002 | NOT USED Answer option to control question d2: ‘Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?’: No | character | 0 | 263 | 263 | 0 | 3 | 2 | 3 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
NOT USED Answer option to control question d2: ‘Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?’: I don’t remember
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | label | data_type | missing | complete | n | empty | n_unique | min | max |
|---|---|---|---|---|---|---|---|---|---|
| d3.sq003 | NOT USED Answer option to control question d2: ‘Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?’: I don’t remember | character | 0 | 263 | 263 | 0 | 3 | 2 | 3 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
Gender: open-entry self-report
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
2 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | label | data_type | missing | complete | n | empty | n_unique | min | max |
|---|---|---|---|---|---|---|---|---|---|
| gender | Gender: open-entry self-report | character | 2 | 261 | 263 | 0 | 11 | 1 | 11 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
Age categories
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
2 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | label | data_type | missing | complete | n | empty | n_unique | min | max |
|---|---|---|---|---|---|---|---|---|---|
| age | Age categories | character | 2 | 261 | 263 | 0 | 8 | 13 | 13 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
237 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | empty | n_unique | min | max |
|---|---|---|---|---|---|---|---|---|
| end | character | 237 | 26 | 263 | 0 | 26 | 1 | 228 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
## Error in if (stats::median(table(x)) == 1) {: missing value where TRUE/FALSE needed
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
## No non-missing values to show.
knitr::opts_chunk$set(fig.height = old_height)
263 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | data_type | missing | complete | n | count | mean |
|---|---|---|---|---|---|---|
| return | logical | 263 | 0 | 263 | 263 | NaN |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
Last page completed by the participant, page 12 and 13 are considered as full participation
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | label | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| lastpage | Last page completed by the participant, page 12 and 13 are considered as full participation | integer | 0 | 263 | 263 | 12.19 | 0.39 | 12 | 12 | 12 | 12 | 13 | ▇▁▁▁▁▁▁▂ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
Randomly attributed study condition. 1 & 2 = not shared, 3 & 4 = shared, 1 & 3 = high anchor in anchoring paradigm, 2 & 4 = low anchor.
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | label | data_type | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| random | Randomly attributed study condition. 1 & 2 = not shared, 3 & 4 = shared, 1 & 3 = high anchor in anchoring paradigm, 2 & 4 = low anchor. | integer | 0 | 263 | 263 | 1.52 | 0.5 | 1 | 1 | 2 | 2 | 2 | ▇▁▁▁▁▁▁▇ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
No data sharing policy consent presented. One participant clicked on ‘I disagree’ but contacted the first author by email to indicate that they had ‘a bug’ and was unable to complete the questionnaire. See manuscript for details
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
0 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | label | data_type | missing | complete | n | empty | n_unique | min | max |
|---|---|---|---|---|---|---|---|---|---|
| cb | No data sharing policy consent presented. One participant clicked on ‘I disagree’ but contacted the first author by email to indicate that they had ‘a bug’ and was unable to complete the questionnaire. See manuscript for details | character | 0 | 263 | 263 | 0 | 2 | 7 | 10 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
Data sharing policy presented
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
## Error in if (stats::median(table(x)) == 1) {: missing value where TRUE/FALSE needed
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
## No non-missing values to show.
knitr::opts_chunk$set(fig.height = old_height)
263 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | label | data_type | missing | complete | n | count | mean |
|---|---|---|---|---|---|---|---|
| ca | Data sharing policy presented | logical | 263 | 0 | 263 | 263 | NaN |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
comprehension question consent 1 (distractor): Will this survey take longer than 10 minutes?
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
1 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | label | data_type | missing | complete | n | empty | n_unique | min | max |
|---|---|---|---|---|---|---|---|---|---|
| mc_1 | comprehension question consent 1 (distractor): Will this survey take longer than 10 minutes? | character | 1 | 262 | 263 | 0 | 3 | 2 | 16 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
comprehension question consent 2 (distractor): Is your data anonymous?
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
1 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | label | data_type | missing | complete | n | empty | n_unique | min | max |
|---|---|---|---|---|---|---|---|---|---|
| mc_2 | comprehension question consent 2 (distractor): Is your data anonymous? | character | 1 | 262 | 263 | 0 | 2 | 2 | 3 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
comprehension question/manipulation check: will your data be shared? correct answer depends on condition: Will your data be shared?
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
1 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | label | data_type | missing | complete | n | empty | n_unique | min | max |
|---|---|---|---|---|---|---|---|---|---|
| mc_3 | comprehension question/manipulation check: will your data be shared? correct answer depends on condition: Will your data be shared? | character | 1 | 262 | 263 | 0 | 3 | 2 | 16 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
comprehension question consent 3 (distractor): Can you stop your participation at any time?
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)
1 missing values.
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
| name | label | data_type | missing | complete | n | empty | n_unique | min | max |
|---|---|---|---|---|---|---|---|---|---|
| mc_4 | comprehension question consent 3 (distractor): Can you stop your participation at any time? | character | 1 | 262 | 263 | 0 | 3 | 2 | 16 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}
Reliability: .
Missing: 1.
old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height),
old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)
if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
likert_plot <- likert_from_items(items)
if (!is.null(likert_plot)) {
graphics::plot(likert_plot)
}
}
knitr::opts_chunk$set(fig.height = old_height)
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
dist_plot <- plot_labelled(scale, scale_name, wrap_at)
choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
suppressMessages( # ignore message about overwriting x axis
dist_plot <- dist_plot +
ggplot2::scale_x_continuous("values",
breaks = breaks,
labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
ggplot2::expand_limits(x = range(breaks)))
}
dist_plot
for (i in seq_along(reliabilities)) {
rel <- reliabilities[[i]]
cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}
coefs <- x$scaleReliability$output$dat %>%
tidyr::gather(index, estimate) %>%
dplyr::filter(index != "n.items", index != "n.observations") %>%
dplyr::mutate(index = stringr::str_to_title(
stringr::str_replace_all(index,
stringr::fixed("."), " ")))
cis <- coefs %>%
dplyr::filter(stringr::str_detect(index, " Ci ")) %>%
tidyr::separate(index, c("index", "hilo"), sep = " Ci ") %>%
tidyr::spread(hilo, estimate)
if (nrow(cis)) {
cis <- cis %>% dplyr::rename(
`Lower 95% CI` = .data$Lo, `Upper 95% CI` = .data$Hi
)
}
coefs_with_cis <- coefs %>%
dplyr::filter(!stringr::str_detect(index, " Ci ")) %>%
dplyr::left_join(cis, by = "index") %>%
dplyr::mutate(index = dplyr::if_else(index == "Glb", "Greatest Lower Bound", .data$index)) %>%
dplyr::arrange(!stringr::str_detect(index, "Omega")) %>%
dplyr::select(Index = .data$index, Estimate = .data$estimate)
pander::pander(coefs_with_cis)
| Index | Estimate |
|---|---|
| Cronbach Alpha | 0.7173 |
| Spearman Brown | 0.7175 |
Positive correlations: 1 out of 1 (100%)
print(x$scatterMatrix$output$scatterMatrix)
x$scatterMatrix$output$scatterMatrix <- no_md()
Detailed output
print(x)
##
## Information about this analysis:
##
## Dataframe: res$dat
## Items: bf_1, bf_7R
## Observations: 262
## Positive correlations: 1 out of 1 (100%)
##
## Estimates assuming interval level:
##
## Spearman Brown coefficient: 0.72
## Cronbach's alpha: 0.72
## Pearson Correlation: 0.56
##
##
## Eigen values: 1.559, 0.441NULL
##
## vars n mean sd median trimmed mad min max range skew kurtosis
## bf_1 1 262 3.77 1.79 4 3.75 1.48 1 7 6 0.09 -1.11
## bf_7R 2 262 3.38 1.73 3 3.29 1.48 1 7 6 0.45 -0.82
## se
## bf_1 0.11
## bf_7R 0.11
for (i in seq_along(names(items))) {
attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))
| name | label | data_type | value_labels | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| bf_1 | TIPI item 1, Extraversion: I see myself as extraverted, enthousiastic. | integer | 1. Disagree strongly, 2. Disagree moderately, 3. Disagree a little, 4. Neither agree nor disagree, 5. Agree a little, 6. Agree moderately, 7. Agree strongly |
1 | 262 | 263 | 3.77 | 1.79 | 1 | 2 | 4 | 5 | 7 | ▅▆▇▅▁▇▅▂ |
| bf_7R | TIPI item 6, Extraversion: I see myself as reserved, quiet. | numeric | 7. Disagree strongly, 6. Disagree moderately, 5. Disagree a little, 4. Neither agree nor disagree, 3. Agree a little, 2. Agree moderately, 1. Agree strongly |
1 | 262 | 263 | 3.38 | 1.73 | 1 | 2 | 3 | 5 | 7 | ▅▆▇▂▁▅▃▂ |
Reliability: .
Missing: 1.
old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height),
old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)
if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
likert_plot <- likert_from_items(items)
if (!is.null(likert_plot)) {
graphics::plot(likert_plot)
}
}
knitr::opts_chunk$set(fig.height = old_height)
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
dist_plot <- plot_labelled(scale, scale_name, wrap_at)
choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
suppressMessages( # ignore message about overwriting x axis
dist_plot <- dist_plot +
ggplot2::scale_x_continuous("values",
breaks = breaks,
labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
ggplot2::expand_limits(x = range(breaks)))
}
dist_plot
for (i in seq_along(reliabilities)) {
rel <- reliabilities[[i]]
cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}
coefs <- x$scaleReliability$output$dat %>%
tidyr::gather(index, estimate) %>%
dplyr::filter(index != "n.items", index != "n.observations") %>%
dplyr::mutate(index = stringr::str_to_title(
stringr::str_replace_all(index,
stringr::fixed("."), " ")))
cis <- coefs %>%
dplyr::filter(stringr::str_detect(index, " Ci ")) %>%
tidyr::separate(index, c("index", "hilo"), sep = " Ci ") %>%
tidyr::spread(hilo, estimate)
if (nrow(cis)) {
cis <- cis %>% dplyr::rename(
`Lower 95% CI` = .data$Lo, `Upper 95% CI` = .data$Hi
)
}
coefs_with_cis <- coefs %>%
dplyr::filter(!stringr::str_detect(index, " Ci ")) %>%
dplyr::left_join(cis, by = "index") %>%
dplyr::mutate(index = dplyr::if_else(index == "Glb", "Greatest Lower Bound", .data$index)) %>%
dplyr::arrange(!stringr::str_detect(index, "Omega")) %>%
dplyr::select(Index = .data$index, Estimate = .data$estimate)
pander::pander(coefs_with_cis)
| Index | Estimate |
|---|---|
| Cronbach Alpha | -0.3971 |
| Spearman Brown | -0.4053 |
Positive correlations: 0 out of 1 (0%)
print(x$scatterMatrix$output$scatterMatrix)
x$scatterMatrix$output$scatterMatrix <- no_md()
Detailed output
print(x)
##
## Information about this analysis:
##
## Dataframe: res$dat
## Items: bf_2R, bf_8
## Observations: 262
## Positive correlations: 0 out of 1 (0%)
##
## Estimates assuming interval level:
##
## Spearman Brown coefficient: -0.41
## Cronbach's alpha: -0.4
## Pearson Correlation: -0.17
##
##
## Eigen values: 1.169, 0.831NULL
##
## vars n mean sd median trimmed mad min max range skew kurtosis
## bf_2R 1 262 4.23 1.61 5 4.32 1.48 1 7 6 -0.43 -0.84
## bf_8 2 262 5.32 1.34 6 5.49 1.48 1 7 6 -1.22 1.55
## se
## bf_2R 0.10
## bf_8 0.08
for (i in seq_along(names(items))) {
attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))
| name | label | data_type | value_labels | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| bf_2R | TIPI item 2, Agreeableness: I see myself as critical, quarrelsome. | numeric | 1. Disagree strongly, 2. Disagree moderately, 3. Disagree a little, 4. Neither agree nor disagree, 5. Agree a little, 6. Agree moderately, 7. Agree strongly |
1 | 262 | 263 | 4.23 | 1.61 | 1 | 3 | 5 | 5 | 7 | ▂▃▃▃▁▇▅▁ |
| bf_8 | TIPI item 7, Agreeableness: I see myself as sympathetic, warm. | integer | 1. Disagree strongly, 2. Disagree moderately, 3. Disagree a little, 4. Neither agree nor disagree, 5. Agree a little, 6. Agree moderately, 7. Agree strongly |
1 | 262 | 263 | 5.32 | 1.34 | 1 | 5 | 6 | 6 | 7 | ▁▁▁▂▁▇▇▃ |
Reliability: .
Missing: 1.
old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height),
old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)
if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
likert_plot <- likert_from_items(items)
if (!is.null(likert_plot)) {
graphics::plot(likert_plot)
}
}
knitr::opts_chunk$set(fig.height = old_height)
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
dist_plot <- plot_labelled(scale, scale_name, wrap_at)
choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
suppressMessages( # ignore message about overwriting x axis
dist_plot <- dist_plot +
ggplot2::scale_x_continuous("values",
breaks = breaks,
labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
ggplot2::expand_limits(x = range(breaks)))
}
dist_plot
for (i in seq_along(reliabilities)) {
rel <- reliabilities[[i]]
cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}
coefs <- x$scaleReliability$output$dat %>%
tidyr::gather(index, estimate) %>%
dplyr::filter(index != "n.items", index != "n.observations") %>%
dplyr::mutate(index = stringr::str_to_title(
stringr::str_replace_all(index,
stringr::fixed("."), " ")))
cis <- coefs %>%
dplyr::filter(stringr::str_detect(index, " Ci ")) %>%
tidyr::separate(index, c("index", "hilo"), sep = " Ci ") %>%
tidyr::spread(hilo, estimate)
if (nrow(cis)) {
cis <- cis %>% dplyr::rename(
`Lower 95% CI` = .data$Lo, `Upper 95% CI` = .data$Hi
)
}
coefs_with_cis <- coefs %>%
dplyr::filter(!stringr::str_detect(index, " Ci ")) %>%
dplyr::left_join(cis, by = "index") %>%
dplyr::mutate(index = dplyr::if_else(index == "Glb", "Greatest Lower Bound", .data$index)) %>%
dplyr::arrange(!stringr::str_detect(index, "Omega")) %>%
dplyr::select(Index = .data$index, Estimate = .data$estimate)
pander::pander(coefs_with_cis)
| Index | Estimate |
|---|---|
| Cronbach Alpha | 0.562 |
| Spearman Brown | 0.5651 |
Positive correlations: 1 out of 1 (100%)
print(x$scatterMatrix$output$scatterMatrix)
x$scatterMatrix$output$scatterMatrix <- no_md()
Detailed output
print(x)
##
## Information about this analysis:
##
## Dataframe: res$dat
## Items: bf_3, bf_10R
## Observations: 262
## Positive correlations: 1 out of 1 (100%)
##
## Estimates assuming interval level:
##
## Spearman Brown coefficient: 0.57
## Cronbach's alpha: 0.56
## Pearson Correlation: 0.39
##
##
## Eigen values: 1.394, 0.606NULL
##
## vars n mean sd median trimmed mad min max range skew kurtosis
## bf_3 1 262 5.03 1.40 5 5.13 1.48 1 7 6 -0.72 0.04
## bf_10R 2 262 5.05 1.59 5 5.15 1.48 1 7 6 -0.47 -0.93
## se
## bf_3 0.09
## bf_10R 0.10
for (i in seq_along(names(items))) {
attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))
| name | label | data_type | value_labels | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| bf_3 | TIPI item 3, Conscientiousness: I see myself as dependable, self-disciplined. | integer | 1. Disagree strongly, 2. Disagree moderately, 3. Disagree a little, 4. Neither agree nor disagree, 5. Agree a little, 6. Agree moderately, 7. Agree strongly |
1 | 262 | 263 | 5.03 | 1.4 | 1 | 4 | 5 | 6 | 7 | ▁▁▂▃▁▇▇▃ |
| bf_10R | TIPI item 8, Conscientiousness: I see myself as disorganized, careless. | numeric | 7. Disagree strongly, 6. Disagree moderately, 5. Disagree a little, 4. Neither agree nor disagree, 3. Agree a little, 2. Agree moderately, 1. Agree strongly |
1 | 262 | 263 | 5.05 | 1.59 | 1 | 4 | 5 | 6 | 7 | ▁▂▅▃▁▅▇▆ |
Reliability: .
Missing: 1.
old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height),
old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)
if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
likert_plot <- likert_from_items(items)
if (!is.null(likert_plot)) {
graphics::plot(likert_plot)
}
}
knitr::opts_chunk$set(fig.height = old_height)
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
dist_plot <- plot_labelled(scale, scale_name, wrap_at)
choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
suppressMessages( # ignore message about overwriting x axis
dist_plot <- dist_plot +
ggplot2::scale_x_continuous("values",
breaks = breaks,
labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
ggplot2::expand_limits(x = range(breaks)))
}
dist_plot
for (i in seq_along(reliabilities)) {
rel <- reliabilities[[i]]
cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}
coefs <- x$scaleReliability$output$dat %>%
tidyr::gather(index, estimate) %>%
dplyr::filter(index != "n.items", index != "n.observations") %>%
dplyr::mutate(index = stringr::str_to_title(
stringr::str_replace_all(index,
stringr::fixed("."), " ")))
cis <- coefs %>%
dplyr::filter(stringr::str_detect(index, " Ci ")) %>%
tidyr::separate(index, c("index", "hilo"), sep = " Ci ") %>%
tidyr::spread(hilo, estimate)
if (nrow(cis)) {
cis <- cis %>% dplyr::rename(
`Lower 95% CI` = .data$Lo, `Upper 95% CI` = .data$Hi
)
}
coefs_with_cis <- coefs %>%
dplyr::filter(!stringr::str_detect(index, " Ci ")) %>%
dplyr::left_join(cis, by = "index") %>%
dplyr::mutate(index = dplyr::if_else(index == "Glb", "Greatest Lower Bound", .data$index)) %>%
dplyr::arrange(!stringr::str_detect(index, "Omega")) %>%
dplyr::select(Index = .data$index, Estimate = .data$estimate)
pander::pander(coefs_with_cis)
| Index | Estimate |
|---|---|
| Cronbach Alpha | -2.327 |
| Spearman Brown | -2.396 |
Positive correlations: 0 out of 1 (0%)
print(x$scatterMatrix$output$scatterMatrix)
x$scatterMatrix$output$scatterMatrix <- no_md()
Detailed output
print(x)
##
## Information about this analysis:
##
## Dataframe: res$dat
## Items: bf_5R, bf_11
## Observations: 262
## Positive correlations: 0 out of 1 (0%)
##
## Estimates assuming interval level:
##
## Spearman Brown coefficient: -2.4
## Cronbach's alpha: -2.33
## Pearson Correlation: -0.54
##
##
## Eigen values: 1.545, 0.455NULL
##
## vars n mean sd median trimmed mad min max range skew kurtosis
## bf_5R 1 262 3.98 1.82 4 3.94 2.97 1 7 6 0.06 -1.17
## bf_11 2 262 4.68 1.54 5 4.75 1.48 1 7 6 -0.43 -0.69
## se
## bf_5R 0.11
## bf_11 0.10
for (i in seq_along(names(items))) {
attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))
| name | label | data_type | value_labels | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| bf_5R | TIPI item 4, Neuroticsm: I see myself as anxious, easily upset. | numeric | 1. Disagree strongly, 2. Disagree moderately, 3. Disagree a little, 4. Neither agree nor disagree, 5. Agree a little, 6. Agree moderately, 7. Agree strongly |
1 | 262 | 263 | 3.98 | 1.82 | 1 | 2 | 4 | 5 | 7 | ▂▇▆▃▁▇▅▃ |
| bf_11 | TIPI item 9, Neuroticsm: I see myself as calm, emotionally stable. | integer | 1. Disagree strongly, 2. Disagree moderately, 3. Disagree a little, 4. Neither agree nor disagree, 5. Agree a little, 6. Agree moderately, 7. Agree strongly |
1 | 262 | 263 | 4.68 | 1.54 | 1 | 4 | 5 | 6 | 7 | ▁▂▅▅▁▇▇▃ |
Reliability: .
Missing: 1.
old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height),
old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)
if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
likert_plot <- likert_from_items(items)
if (!is.null(likert_plot)) {
graphics::plot(likert_plot)
}
}
knitr::opts_chunk$set(fig.height = old_height)
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
dist_plot <- plot_labelled(scale, scale_name, wrap_at)
choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
suppressMessages( # ignore message about overwriting x axis
dist_plot <- dist_plot +
ggplot2::scale_x_continuous("values",
breaks = breaks,
labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
ggplot2::expand_limits(x = range(breaks)))
}
dist_plot
for (i in seq_along(reliabilities)) {
rel <- reliabilities[[i]]
cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}
coefs <- x$scaleReliability$output$dat %>%
tidyr::gather(index, estimate) %>%
dplyr::filter(index != "n.items", index != "n.observations") %>%
dplyr::mutate(index = stringr::str_to_title(
stringr::str_replace_all(index,
stringr::fixed("."), " ")))
cis <- coefs %>%
dplyr::filter(stringr::str_detect(index, " Ci ")) %>%
tidyr::separate(index, c("index", "hilo"), sep = " Ci ") %>%
tidyr::spread(hilo, estimate)
if (nrow(cis)) {
cis <- cis %>% dplyr::rename(
`Lower 95% CI` = .data$Lo, `Upper 95% CI` = .data$Hi
)
}
coefs_with_cis <- coefs %>%
dplyr::filter(!stringr::str_detect(index, " Ci ")) %>%
dplyr::left_join(cis, by = "index") %>%
dplyr::mutate(index = dplyr::if_else(index == "Glb", "Greatest Lower Bound", .data$index)) %>%
dplyr::arrange(!stringr::str_detect(index, "Omega")) %>%
dplyr::select(Index = .data$index, Estimate = .data$estimate)
pander::pander(coefs_with_cis)
| Index | Estimate |
|---|---|
| Cronbach Alpha | 0.444 |
| Spearman Brown | 0.4521 |
Positive correlations: 1 out of 1 (100%)
print(x$scatterMatrix$output$scatterMatrix)
x$scatterMatrix$output$scatterMatrix <- no_md()
Detailed output
print(x)
##
## Information about this analysis:
##
## Dataframe: res$dat
## Items: bf_6, bf_12R
## Observations: 262
## Positive correlations: 1 out of 1 (100%)
##
## Estimates assuming interval level:
##
## Spearman Brown coefficient: 0.45
## Cronbach's alpha: 0.44
## Pearson Correlation: 0.29
##
##
## Eigen values: 1.292, 0.708NULL
##
## vars n mean sd median trimmed mad min max range skew kurtosis
## bf_6 1 262 5.29 1.22 5 5.39 1.48 1 7 6 -0.78 0.85
## bf_12R 2 262 4.79 1.51 5 4.88 1.48 1 7 6 -0.52 -0.56
## se
## bf_6 0.08
## bf_12R 0.09
for (i in seq_along(names(items))) {
attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))
| name | label | data_type | value_labels | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| bf_6 | TIPI item 5, Openness to experience: I see myself as open to new experiences, complex. | integer | 1. Disagree strongly, 2. Disagree moderately, 3. Disagree a little, 4. Neither agree nor disagree, 5. Agree a little, 6. Agree moderately, 7. Agree strongly |
1 | 262 | 263 | 5.29 | 1.22 | 1 | 5 | 5 | 6 | 7 | ▁▁▁▂▁▇▆▃ |
| bf_12R | TIPI item 10, Openness to experience: I see myself as conventional, uncreative. | numeric | 7. Disagree strongly, 6. Disagree moderately, 5. Disagree a little, 4. Neither agree nor disagree, 3. Agree a little, 2. Agree moderately, 1. Agree strongly |
1 | 262 | 263 | 4.79 | 1.51 | 1 | 4 | 5 | 6 | 7 | ▁▂▃▅▁▆▇▃ |
Reliability: ωordinal [95% CI] = 0.28 [0.18;0.37].
Missing: 1.
old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height),
old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)
if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
likert_plot <- likert_from_items(items)
if (!is.null(likert_plot)) {
graphics::plot(likert_plot)
}
}
knitr::opts_chunk$set(fig.height = old_height)
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
dist_plot <- plot_labelled(scale, scale_name, wrap_at)
choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
suppressMessages( # ignore message about overwriting x axis
dist_plot <- dist_plot +
ggplot2::scale_x_continuous("values",
breaks = breaks,
labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
ggplot2::expand_limits(x = range(breaks)))
}
dist_plot
for (i in seq_along(reliabilities)) {
rel <- reliabilities[[i]]
cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}
coefs <- x$scaleReliability$output$dat %>%
tidyr::gather(index, estimate) %>%
dplyr::filter(index != "n.items", index != "n.observations") %>%
dplyr::mutate(index = stringr::str_to_title(
stringr::str_replace_all(index,
stringr::fixed("."), " ")))
cis <- coefs %>%
dplyr::filter(stringr::str_detect(index, " Ci ")) %>%
tidyr::separate(index, c("index", "hilo"), sep = " Ci ") %>%
tidyr::spread(hilo, estimate)
if (nrow(cis)) {
cis <- cis %>% dplyr::rename(
`Lower 95% CI` = .data$Lo, `Upper 95% CI` = .data$Hi
)
}
coefs_with_cis <- coefs %>%
dplyr::filter(!stringr::str_detect(index, " Ci ")) %>%
dplyr::left_join(cis, by = "index") %>%
dplyr::mutate(index = dplyr::if_else(index == "Glb", "Greatest Lower Bound", .data$index)) %>%
dplyr::arrange(!stringr::str_detect(index, "Omega")) %>%
dplyr::select(Index = .data$index, Estimate = .data$estimate)
pander::pander(coefs_with_cis)
| Index | Estimate |
|---|---|
| Omega | 0.1378 |
| Omega Psych Tot | 0.6496 |
| Omega Psych H | 0.5475 |
| Omega Ordinal | 0.2761 |
| Cronbach Alpha | 0.5032 |
| Greatest Lower Bound | 0.7487 |
| Alpha Ordinal | 0.6437 |
Positive correlations: 313 out of 528 (59%)
print(x$scatterMatrix$output$scatterMatrix)
x$scatterMatrix$output$scatterMatrix <- no_md()
Detailed output
print(x)
##
## Information about this analysis:
##
## Dataframe: res$dat
## Items: soc_d_1, soc_d_2, soc_d_3R, soc_d_4, soc_d_5R, soc_d_6R, soc_d_7, soc_d_8, soc_d_9R, soc_d_10R, soc_d_11R, soc_d_12R, soc_d_13, soc_d_14R, soc_d_15R, soc_d_16, soc_d_17, soc_d_18, soc_d_19R, soc_d_20, soc_d_21, soc_d_22R, soc_d_23R, soc_d_24, soc_d_25, soc_d_26, soc_d_27, soc_d_28R, soc_d_29, soc_d_30R, soc_d_31, soc_d_32R, soc_d_33
## Observations: 262
## Positive correlations: 313 out of 528 (59%)
##
## Estimates assuming interval level:
##
## Omega (total): 0.14
## Omega (hierarchical): 0.55
## Revelle's omega (total): 0.65
## Greatest Lower Bound (GLB): 0.75
## Coefficient H: 0.75
## Cronbach's alpha: 0.5
## Confidence intervals:
## Omega (total): [0.03, 0.25]
## Cronbach's alpha: [0.42, 0.59]
##
## Estimates assuming ordinal level:
##
## Ordinal Omega (total): 0.28
## Ordinal Omega (hierarch.): 0.12
## Ordinal Cronbach's alpha: 0.64
## Confidence intervals:
## Ordinal Omega (total): [0.18, 0.37]
## Ordinal Cronbach's alpha: [0.58, 0.71]
##
## Note: the normal point estimate and confidence interval for omega are based on the procedure suggested by Dunn, Baguley & Brunsden (2013) using the MBESS function ci.reliability, whereas the psych package point estimate was suggested in Revelle & Zinbarg (2008). See the help ('?scaleStructure') for more information.
##
## Eigen values: 3.475, 2.2, 1.683, 1.644, 1.446, 1.355, 1.308, 1.27, 1.165, 1.12, 1.103, 1.013, 0.976, 0.93, 0.926, 0.891, 0.846, 0.797, 0.79, 0.745, 0.707, 0.697, 0.675, 0.651, 0.63, 0.595, 0.586, 0.534, 0.51, 0.484, 0.481, 0.404, 0.363
## Loadings:
## TC11 TC12 TC1 TC2 TC7 TC5 TC4 TC10 TC8
## soc_d_1 0.147
## soc_d_2 -0.183 -0.182 0.114 0.241 0.372 0.294
## soc_d_3R 0.737 0.106 0.154
## soc_d_4 -0.119 0.133 0.265 -0.467 -0.303
## soc_d_5R 0.153 0.200 -0.140 0.217
## soc_d_6R 0.728 -0.170
## soc_d_7 -0.168 0.256 -0.240 0.142 -0.243 0.259 0.202
## soc_d_8 -0.497 0.237 0.126 0.296 -0.177
## soc_d_9R 0.187 0.473 0.142 -0.218
## soc_d_10R 0.535 -0.124 0.141 0.187 0.237
## soc_d_11R -0.192 0.170 -0.259 0.194 0.388 0.280
## soc_d_12R 0.152 0.707 -0.157
## soc_d_13 -0.228 0.167 0.212 -0.378 0.123
## soc_d_14R 0.282 0.253 -0.119 0.237 0.168 0.338 -0.258
## soc_d_15R -0.100 0.520 0.400 0.132 -0.180
## soc_d_16 -0.308 0.236 0.411 0.135 0.283
## soc_d_17 -0.106 0.812 -0.150
## soc_d_18 0.195 0.223 0.172 -0.171 -0.455
## soc_d_19R 0.106 -0.130 -0.166 -0.110 0.428 -0.106
## soc_d_20 0.193 0.180 0.592 -0.132 0.297
## soc_d_21 0.179 0.122 -0.179 0.250 -0.243 -0.183
## soc_d_22R 0.415 0.217 0.211 0.164
## soc_d_23R 0.818
## soc_d_24 0.104 -0.103 0.110 -0.156 0.694
## soc_d_25 -0.149 0.773 0.115
## soc_d_26 0.152 -0.146 0.512
## soc_d_27 -0.108 0.103
## soc_d_28R 0.265 0.170 0.423 0.140 0.150 0.114 0.180
## soc_d_29 0.228 -0.216 0.200 0.303 0.376 -0.222
## soc_d_30R 0.226 0.484 0.127 -0.331 0.121 0.131
## soc_d_31 0.192 -0.211 0.731 -0.116
## soc_d_32R 0.731 0.106
## soc_d_33 -0.211 0.715 0.166
## TC9 TC3 TC6
## soc_d_1 0.786 -0.131
## soc_d_2 0.253
## soc_d_3R
## soc_d_4 -0.185 -0.178
## soc_d_5R 0.231 -0.607
## soc_d_6R -0.113
## soc_d_7 0.328 0.220 0.166
## soc_d_8 0.139 -0.178
## soc_d_9R 0.164 0.380
## soc_d_10R 0.112 -0.202 -0.199
## soc_d_11R -0.166 0.299
## soc_d_12R 0.192
## soc_d_13 0.306 -0.189 0.336
## soc_d_14R -0.101 -0.120 0.245
## soc_d_15R 0.149 -0.127
## soc_d_16 0.141
## soc_d_17
## soc_d_18 0.207 0.270
## soc_d_19R 0.178 0.510
## soc_d_20 -0.135 -0.107
## soc_d_21 0.328 -0.286 0.329
## soc_d_22R 0.465
## soc_d_23R -0.110
## soc_d_24 0.108
## soc_d_25 -0.131
## soc_d_26 0.290 0.123 0.143
## soc_d_27 0.124 -0.747
## soc_d_28R -0.164
## soc_d_29 0.153 -0.171
## soc_d_30R 0.160
## soc_d_31
## soc_d_32R
## soc_d_33
##
## TC11 TC12 TC1 TC2 TC7 TC5 TC4 TC10 TC8 TC9
## SS loadings 1.655 1.626 1.600 1.586 1.579 1.538 1.511 1.507 1.460 1.425
## Proportion Var 0.050 0.049 0.048 0.048 0.048 0.047 0.046 0.046 0.044 0.043
## Cumulative Var 0.050 0.099 0.148 0.196 0.244 0.290 0.336 0.382 0.426 0.469
## TC3 TC6
## SS loadings 1.367 1.359
## Proportion Var 0.041 0.041
## Cumulative Var 0.511 0.552
##
## vars n mean sd median trimmed mad min max range skew
## soc_d_1 1 262 0.67 0.47 1 0.71 0 0 1 1 -0.73
## soc_d_2 2 262 0.68 0.47 1 0.72 0 0 1 1 -0.75
## soc_d_3R 3 262 0.65 0.48 1 0.69 0 0 1 1 -0.64
## soc_d_4 4 262 0.21 0.41 0 0.14 0 0 1 1 1.42
## soc_d_5R 5 262 0.84 0.37 1 0.92 0 0 1 1 -1.80
## soc_d_6R 6 262 0.70 0.46 1 0.75 0 0 1 1 -0.86
## soc_d_7 7 262 0.49 0.50 0 0.49 0 0 1 1 0.03
## soc_d_8 8 262 0.53 0.50 1 0.54 0 0 1 1 -0.12
## soc_d_9R 9 262 0.53 0.50 1 0.54 0 0 1 1 -0.14
## soc_d_10R 10 262 0.73 0.45 1 0.79 0 0 1 1 -1.02
## soc_d_11R 11 262 0.66 0.47 1 0.70 0 0 1 1 -0.69
## soc_d_12R 12 262 0.51 0.50 1 0.51 0 0 1 1 -0.03
## soc_d_13 13 262 0.72 0.45 1 0.78 0 0 1 1 -0.98
## soc_d_14R 14 262 0.72 0.45 1 0.78 0 0 1 1 -0.98
## soc_d_15R 15 262 0.58 0.50 1 0.60 0 0 1 1 -0.31
## soc_d_16 16 262 0.63 0.48 1 0.67 0 0 1 1 -0.55
## soc_d_17 17 262 0.85 0.35 1 0.94 0 0 1 1 -2.00
## soc_d_18 18 262 0.26 0.44 0 0.20 0 0 1 1 1.11
## soc_d_19R 19 262 0.56 0.50 1 0.57 0 0 1 1 -0.23
## soc_d_20 20 262 0.79 0.41 1 0.86 0 0 1 1 -1.42
## soc_d_21 21 262 0.63 0.48 1 0.66 0 0 1 1 -0.53
## soc_d_22R 22 262 0.80 0.40 1 0.87 0 0 1 1 -1.47
## soc_d_23R 23 262 0.84 0.36 1 0.93 0 0 1 1 -1.88
## soc_d_24 24 262 0.82 0.38 1 0.90 0 0 1 1 -1.66
## soc_d_25 25 262 0.72 0.45 1 0.77 0 0 1 1 -0.96
## soc_d_26 26 262 0.40 0.49 0 0.37 0 0 1 1 0.42
## soc_d_27 27 262 0.51 0.50 1 0.51 0 0 1 1 -0.03
## soc_d_28R 28 262 0.84 0.36 1 0.93 0 0 1 1 -1.88
## soc_d_29 29 262 0.27 0.44 0 0.21 0 0 1 1 1.05
## soc_d_30R 30 262 0.58 0.49 1 0.60 0 0 1 1 -0.34
## soc_d_31 31 262 0.26 0.44 0 0.20 0 0 1 1 1.11
## soc_d_32R 32 262 0.47 0.50 0 0.47 0 0 1 1 0.11
## soc_d_33 33 262 0.30 0.46 0 0.25 0 0 1 1 0.88
## kurtosis se
## soc_d_1 -1.48 0.03
## soc_d_2 -1.45 0.03
## soc_d_3R -1.60 0.03
## soc_d_4 0.01 0.03
## soc_d_5R 1.26 0.02
## soc_d_6R -1.27 0.03
## soc_d_7 -2.01 0.03
## soc_d_8 -1.99 0.03
## soc_d_9R -1.99 0.03
## soc_d_10R -0.95 0.03
## soc_d_11R -1.53 0.03
## soc_d_12R -2.01 0.03
## soc_d_13 -1.04 0.03
## soc_d_14R -1.04 0.03
## soc_d_15R -1.91 0.03
## soc_d_16 -1.70 0.03
## soc_d_17 2.03 0.02
## soc_d_18 -0.76 0.03
## soc_d_19R -1.95 0.03
## soc_d_20 0.01 0.03
## soc_d_21 -1.72 0.03
## soc_d_22R 0.17 0.02
## soc_d_23R 1.54 0.02
## soc_d_24 0.76 0.02
## soc_d_25 -1.08 0.03
## soc_d_26 -1.83 0.03
## soc_d_27 -2.01 0.03
## soc_d_28R 1.54 0.02
## soc_d_29 -0.91 0.03
## soc_d_30R -1.89 0.03
## soc_d_31 -0.76 0.03
## soc_d_32R -2.00 0.03
## soc_d_33 -1.23 0.03
for (i in seq_along(names(items))) {
attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))
| name | label | data_type | value_labels | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| soc_d_1 | Social desirability questionnaire item 1: Before voting I thoroughly investigate the qualifications of all the candidates. | integer | 0. No, 1. Yes |
1 | 262 | 263 | 0.67 | 0.47 | 0 | 0 | 1 | 1 | 1 | ▃▁▁▁▁▁▁▇ |
| soc_d_2 | Social desirability questionnaire item 2: I never hesitate to go out of my way to help someone in trouble. | integer | 0. No, 1. Yes |
1 | 262 | 263 | 0.68 | 0.47 | 0 | 0 | 1 | 1 | 1 | ▃▁▁▁▁▁▁▇ |
| soc_d_3R | Social desirability questionnaire item 3: It is sometimes hard for me to go on with my work if I am not encouraged. | numeric | 0. No, 1. Yes |
1 | 262 | 263 | 0.65 | 0.48 | 0 | 0 | 1 | 1 | 1 | ▅▁▁▁▁▁▁▇ |
| soc_d_4 | Social desirability questionnaire item 4: I have never intensely disliked anyone. | integer | 0. No, 1. Yes |
1 | 262 | 263 | 0.21 | 0.41 | 0 | 0 | 0 | 0 | 1 | ▇▁▁▁▁▁▁▂ |
| soc_d_5R | Social desirability questionnaire item 5: On occasion I have had doubts about my ability to succeed in life. | numeric | 0. No, 1. Yes |
1 | 262 | 263 | 0.84 | 0.37 | 0 | 1 | 1 | 1 | 1 | ▂▁▁▁▁▁▁▇ |
| soc_d_6R | Social desirability questionnaire item 6: I sometimes feel resentful when I don’t get my way. | numeric | 0. No, 1. Yes |
1 | 262 | 263 | 0.7 | 0.46 | 0 | 0 | 1 | 1 | 1 | ▃▁▁▁▁▁▁▇ |
| soc_d_7 | Social desirability questionnaire item 7: I am always careful about my manner of dress. | integer | 0. No, 1. Yes |
1 | 262 | 263 | 0.49 | 0.5 | 0 | 0 | 0 | 1 | 1 | ▇▁▁▁▁▁▁▇ |
| soc_d_8 | Social desirability questionnaire item 8: My table manners at home are as good as when I eat out in a restaurant. | integer | 0. No, 1. Yes |
1 | 262 | 263 | 0.53 | 0.5 | 0 | 0 | 1 | 1 | 1 | ▇▁▁▁▁▁▁▇ |
| soc_d_9R | Social desirability questionnaire item 9: If I could get into a movie without paying and be sure I was not seen I would probably do it. | numeric | 0. No, 1. Yes |
1 | 262 | 263 | 0.53 | 0.5 | 0 | 0 | 1 | 1 | 1 | ▇▁▁▁▁▁▁▇ |
| soc_d_10R | Social desirability questionnaire item 10: On a few occasions, I have given up doing something because I thought too little of my ability. | numeric | 0. No, 1. Yes |
1 | 262 | 263 | 0.73 | 0.45 | 0 | 0 | 1 | 1 | 1 | ▃▁▁▁▁▁▁▇ |
| soc_d_11R | Social desirability questionnaire item 11: I like to gossip at times. | numeric | 0. No, 1. Yes |
1 | 262 | 263 | 0.66 | 0.47 | 0 | 0 | 1 | 1 | 1 | ▅▁▁▁▁▁▁▇ |
| soc_d_12R | Social desirability questionnaire item 12: There have been times when I felt like rebelling against people in authority even though I knew they were right. | numeric | 0. No, 1. Yes |
1 | 262 | 263 | 0.51 | 0.5 | 0 | 0 | 1 | 1 | 1 | ▇▁▁▁▁▁▁▇ |
| soc_d_13 | Social desirability questionnaire item 13: No matter who I’m talking to, I’m always a good listener. | integer | 0. No, 1. Yes |
1 | 262 | 263 | 0.72 | 0.45 | 0 | 0 | 1 | 1 | 1 | ▃▁▁▁▁▁▁▇ |
| soc_d_14R | Social desirability questionnaire item 14: I can remember ‘playing sick’ to get out of something. | numeric | 0. No, 1. Yes |
1 | 262 | 263 | 0.72 | 0.45 | 0 | 0 | 1 | 1 | 1 | ▃▁▁▁▁▁▁▇ |
| soc_d_15R | Social desirability questionnaire item 15: There have been occasions when I took advantage of someone. | numeric | 0. No, 1. Yes |
1 | 262 | 263 | 0.58 | 0.5 | 0 | 0 | 1 | 1 | 1 | ▆▁▁▁▁▁▁▇ |
| soc_d_16 | Social desirability questionnaire item 16: I’m always willing to admit it when I make a mistake. | integer | 0. No, 1. Yes |
1 | 262 | 263 | 0.63 | 0.48 | 0 | 0 | 1 | 1 | 1 | ▅▁▁▁▁▁▁▇ |
| soc_d_17 | Social desirability questionnaire item 17: I always try to practice what I preach. | integer | 0. No, 1. Yes |
1 | 262 | 263 | 0.85 | 0.35 | 0 | 1 | 1 | 1 | 1 | ▂▁▁▁▁▁▁▇ |
| soc_d_18 | Social desirability questionnaire item 18: I don’t find it particularly difficult to get along with loud mouthed, obnoxious people. | integer | 0. No, 1. Yes |
1 | 262 | 263 | 0.26 | 0.44 | 0 | 0 | 0 | 1 | 1 | ▇▁▁▁▁▁▁▃ |
| soc_d_19R | Social desirability questionnaire item 19: I sometimes try to get even rather than forgive and forget. | numeric | 0. No, 1. Yes |
1 | 262 | 263 | 0.56 | 0.5 | 0 | 0 | 1 | 1 | 1 | ▆▁▁▁▁▁▁▇ |
| soc_d_20 | Social desirability questionnaire item 20: When I don’t know something I don’t at all mind admitting it. | integer | 0. No, 1. Yes |
1 | 262 | 263 | 0.79 | 0.41 | 0 | 1 | 1 | 1 | 1 | ▂▁▁▁▁▁▁▇ |
| soc_d_21 | Social desirability questionnaire item 21: I am always courteous, even to people who are disagreeable. | integer | 0. No, 1. Yes |
1 | 262 | 263 | 0.63 | 0.48 | 0 | 0 | 1 | 1 | 1 | ▅▁▁▁▁▁▁▇ |
| soc_d_22R | Social desirability questionnaire item 22: At times I have really insisted on having things my own way. | numeric | 0. No, 1. Yes |
1 | 262 | 263 | 0.8 | 0.4 | 0 | 1 | 1 | 1 | 1 | ▂▁▁▁▁▁▁▇ |
| soc_d_23R | Social desirability questionnaire item 23: There have been occasions when I felt like smashing things. | numeric | 0. No, 1. Yes |
1 | 262 | 263 | 0.84 | 0.36 | 0 | 1 | 1 | 1 | 1 | ▂▁▁▁▁▁▁▇ |
| soc_d_24 | Social desirability questionnaire item 24: I would never think of letting someone else be punished for my wrong- doings. | integer | 0. No, 1. Yes |
1 | 262 | 263 | 0.82 | 0.38 | 0 | 1 | 1 | 1 | 1 | ▂▁▁▁▁▁▁▇ |
| soc_d_25 | Social desirability questionnaire item 25: I never resent being asked to return a favor. | integer | 0. No, 1. Yes |
1 | 262 | 263 | 0.72 | 0.45 | 0 | 0 | 1 | 1 | 1 | ▃▁▁▁▁▁▁▇ |
| soc_d_26 | Social desirability questionnaire item 26: I have never been irked when people expressed ideas very different from my own. | integer | 0. No, 1. Yes |
1 | 262 | 263 | 0.4 | 0.49 | 0 | 0 | 0 | 1 | 1 | ▇▁▁▁▁▁▁▅ |
| soc_d_27 | Social desirability questionnaire item 27: I never make a long trip without checking the safety of my car. | integer | 0. No, 1. Yes |
1 | 262 | 263 | 0.51 | 0.5 | 0 | 0 | 1 | 1 | 1 | ▇▁▁▁▁▁▁▇ |
| soc_d_28R | Social desirability questionnaire item 28: There have been times when I was quite jealous of the good fortune of others. | numeric | 0. No, 1. Yes |
1 | 262 | 263 | 0.84 | 0.36 | 0 | 1 | 1 | 1 | 1 | ▂▁▁▁▁▁▁▇ |
| soc_d_29 | Social desirability questionnaire item 29: I have almost never felt the urge to tell someone off. | integer | 0. No, 1. Yes |
1 | 262 | 263 | 0.27 | 0.44 | 0 | 0 | 0 | 1 | 1 | ▇▁▁▁▁▁▁▃ |
| soc_d_30R | Social desirability questionnaire item 30: I am sometimes irritated by people who ask favors of me. | numeric | 0. No, 1. Yes |
1 | 262 | 263 | 0.58 | 0.49 | 0 | 0 | 1 | 1 | 1 | ▆▁▁▁▁▁▁▇ |
| soc_d_31 | Social desirability questionnaire item 31: I have never felt that I was punished without cause. | integer | 0. No, 1. Yes |
1 | 262 | 263 | 0.26 | 0.44 | 0 | 0 | 0 | 1 | 1 | ▇▁▁▁▁▁▁▃ |
| soc_d_32R | Social desirability questionnaire item 32: I sometimes think when people have a misfortune they only got what they deserved. | numeric | 0. No, 1. Yes |
1 | 262 | 263 | 0.47 | 0.5 | 0 | 0 | 0 | 1 | 1 | ▇▁▁▁▁▁▁▇ |
| soc_d_33 | Social desirability questionnaire item 33: I have never deliberately said something that hurt someone’s feelings. | integer | 0. No, 1. Yes |
1 | 262 | 263 | 0.3 | 0.46 | 0 | 0 | 0 | 1 | 1 | ▇▁▁▁▁▁▁▃ |
Reliability: ωtotal [95% CI] = 0.14 [not computed].
Missing: 1.
old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height),
old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)
if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
likert_plot <- likert_from_items(items)
if (!is.null(likert_plot)) {
graphics::plot(likert_plot)
}
}
knitr::opts_chunk$set(fig.height = old_height)
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
dist_plot <- plot_labelled(scale, scale_name, wrap_at)
choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
suppressMessages( # ignore message about overwriting x axis
dist_plot <- dist_plot +
ggplot2::scale_x_continuous("values",
breaks = breaks,
labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
ggplot2::expand_limits(x = range(breaks)))
}
dist_plot
for (i in seq_along(reliabilities)) {
rel <- reliabilities[[i]]
cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}
coefs <- x$scaleReliability$output$dat %>%
tidyr::gather(index, estimate) %>%
dplyr::filter(index != "n.items", index != "n.observations") %>%
dplyr::mutate(index = stringr::str_to_title(
stringr::str_replace_all(index,
stringr::fixed("."), " ")))
cis <- coefs %>%
dplyr::filter(stringr::str_detect(index, " Ci ")) %>%
tidyr::separate(index, c("index", "hilo"), sep = " Ci ") %>%
tidyr::spread(hilo, estimate)
if (nrow(cis)) {
cis <- cis %>% dplyr::rename(
`Lower 95% CI` = .data$Lo, `Upper 95% CI` = .data$Hi
)
}
coefs_with_cis <- coefs %>%
dplyr::filter(!stringr::str_detect(index, " Ci ")) %>%
dplyr::left_join(cis, by = "index") %>%
dplyr::mutate(index = dplyr::if_else(index == "Glb", "Greatest Lower Bound", .data$index)) %>%
dplyr::arrange(!stringr::str_detect(index, "Omega")) %>%
dplyr::select(Index = .data$index, Estimate = .data$estimate)
pander::pander(coefs_with_cis)
| Index | Estimate |
|---|---|
| Omega | 0.1356 |
| Omega Psych Tot | 0.5682 |
| Omega Psych H | 0.2626 |
| Cronbach Alpha | 0.1181 |
| Greatest Lower Bound | 0.6327 |
Positive correlations: 12 out of 21 (57%)
print(x$scatterMatrix$output$scatterMatrix)
x$scatterMatrix$output$scatterMatrix <- no_md()
Detailed output
print(x)
##
## Information about this analysis:
##
## Dataframe: res$dat
## Items: cr_1R, cr_2, cr_3, cr_4, cr_5, cr_6, cr_7
## Observations: 262
## Positive correlations: 12 out of 21 (57%)
##
## Estimates assuming interval level:
##
## Omega (total): 0.14
## Omega (hierarchical): 0.26
## Revelle's omega (total): 0.57
## Greatest Lower Bound (GLB): 0.63
## Coefficient H: 1
## Cronbach's alpha: 0.12
##
## Estimates assuming ordinal level:
##
## Ordinal Omega (total): NA
## Ordinal Omega (hierarch.): NA
## Ordinal Cronbach's alpha: 0.62
##
## Note: the normal point estimate and confidence interval for omega are based on the procedure suggested by Dunn, Baguley & Brunsden (2013) using the MBESS function ci.reliability, whereas the psych package point estimate was suggested in Revelle & Zinbarg (2008). See the help ('?scaleStructure') for more information.
##
## Eigen values: 1.791, 1.419, 1.102, 0.851, 0.786, 0.626, 0.426
## Loadings:
## TC1 TC3 TC2
## cr_1R 0.138 -0.801
## cr_2 0.621 0.101
## cr_3 0.798 -0.155
## cr_4 -0.126 0.805
## cr_5 0.546 0.483
## cr_6 0.867
## cr_7 0.122 0.774
##
## TC1 TC3 TC2
## SS loadings 1.713 1.315 1.265
## Proportion Var 0.245 0.188 0.181
## Cumulative Var 0.245 0.433 0.613
##
## vars n mean sd median trimmed mad min max range skew kurtosis
## cr_1R 1 262 0.91 0.29 1 1.00 0 0 1 1 -2.82 5.95
## cr_2 2 262 0.16 0.37 0 0.08 0 0 1 1 1.84 1.40
## cr_3 3 262 0.03 0.17 0 0.00 0 0 1 1 5.43 27.55
## cr_4 4 262 0.08 0.27 0 0.00 0 0 1 1 3.07 7.48
## cr_5 5 262 0.01 0.11 0 0.00 0 0 1 1 9.13 81.69
## cr_6 6 262 0.01 0.09 0 0.00 0 0 1 1 11.25 125.02
## cr_7 7 262 0.05 0.22 0 0.00 0 0 1 1 4.12 15.07
## se
## cr_1R 0.02
## cr_2 0.02
## cr_3 0.01
## cr_4 0.02
## cr_5 0.01
## cr_6 0.01
## cr_7 0.01
for (i in seq_along(names(items))) {
attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))
| name | label | data_type | value_labels | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| cr_1R | Careless response item 1: I am using an electronic device at this moment. | numeric | 1. Yes, 0. No |
1 | 262 | 263 | 0.91 | 0.29 | 0 | 1 | 1 | 1 | 1 | ▁▁▁▁▁▁▁▇ |
| cr_2 | Careless response item 2: I turn into a leprechaun at night. | integer | 0. Yes, 1. No |
1 | 262 | 263 | 0.16 | 0.37 | 0 | 0 | 0 | 0 | 1 | ▇▁▁▁▁▁▁▂ |
| cr_3 | Careless response item 3: All my friends are aliens. | integer | 0. Yes, 1. No |
1 | 262 | 263 | 0.031 | 0.17 | 0 | 0 | 0 | 0 | 1 | ▇▁▁▁▁▁▁▁ |
| cr_4 | Careless response item 4: All my friends say I would make a great poodle. | integer | 0. Yes, 1. No |
1 | 262 | 263 | 0.08 | 0.27 | 0 | 0 | 0 | 0 | 1 | ▇▁▁▁▁▁▁▁ |
| cr_5 | Careless response item 5: I eat cement occasionally. | integer | 0. Yes, 1. No |
1 | 262 | 263 | 0.011 | 0.11 | 0 | 0 | 0 | 0 | 1 | ▇▁▁▁▁▁▁▁ |
| cr_6 | Careless response item 6: I can teleport across time and space. | integer | 0. Yes, 1. No |
1 | 262 | 263 | 0.0076 | 0.087 | 0 | 0 | 0 | 0 | 1 | ▇▁▁▁▁▁▁▁ |
| cr_7 | Careless response item 7: I will be punished for meeting the requirements of my job. | integer | 0. Yes, 1. No |
1 | 262 | 263 | 0.05 | 0.22 | 0 | 0 | 0 | 0 | 1 | ▇▁▁▁▁▁▁▁ |
missingness_report
if (length(md_pattern)) {
if (knitr::is_html_output()) {
rmarkdown::paged_table(md_pattern, options = list(rows.print = 10))
} else {
knitr::kable(md_pattern)
}
}
items
export_table(metadata_table)
jsonld
JSON-LD metadata
The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.
{
"name": "Online (Prolific.co) data on TIPI, Social Desirability, Careless Response and Anchoring Paradigm, confidential data set",
"description": "10 items taking from the Very brief measure of the Big 5 Personality questionnaire (Gosling, Rentfrow, & Swann, 2003), 33 items from the Social desirability scale (Crowne & Marlowe, 1960) and 3 Anchoring paradigm items as used in the ManyLabs replication project (Klein et al., 2014). Also includes 7 careless response items based on Meade and Craig (2012). This dataset cannot be publicly shared, as the study consent for the participants represented in this data set stated that we would not provide public access to the data. If you are interested in re-analyzing the entire dataset, please contact the authors. Please find the shareable half of the data set on our osf.io page (see doi)\n\n\n## Table of variables\nThis table contains variable names, labels, their central tendencies and other attributes.\n\n|name |label |data_type |value_labels |scale_item_names |missing |complete |n |empty |n_unique |count |min |max |mean |sd |p0 |p25 |p50 |p75 |p100 |hist |\n|:----------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------|:------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------|:--------|:---|:-----|:--------|:-----|:---|:---|:---------|:----------|:-----|:-----|:-----|:-------|:-------|:--------|\n|V1 |NA |integer |NA |NA |0 |263 |263 |NA |NA |NA |NA |NA |297.31 |171.15 |1 |149.5 |300 |445 |576 |▇▆▇▆▇▇▇▇ |\n|id |ID variable from raw data |integer |NA |NA |0 |263 |263 |NA |NA |NA |NA |NA |328.89 |192.86 |1 |161.5 |330 |495 |654 |▇▇▇▇▇▇▇▇ |\n|consent |Variables cb and ca combined in one variable |character |NA |NA |0 |263 |263 |0 |1 |NA |1 |1 |NA |NA |NA |NA |NA |NA |NA |NA |\n|cond_anc |Anchoring condition: high and low |character |NA |NA |0 |263 |263 |0 |2 |NA |1 |1 |NA |NA |NA |NA |NA |NA |NA |NA |\n|refused |Refusal to participate, one participant clicked on 'I disagree' but contacted the first author by email to indicate that they had 'a bug' and was unable to complete the questionnaire. This participant was in the 'no data sharing' condition. |integer |NA |NA |0 |263 |263 |NA |NA |NA |NA |NA |0.0038 |0.062 |0 |0 |0 |0 |1 |▇▁▁▁▁▁▁▁ |\n|remember |Mutated variable from consent and mc_3: Does the participant remember the correct data sharing policy? |integer |0. No or wrong memory, - 1. correct memory |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.73 |0.44 |0 |0 |1 |1 |1 |▃▁▁▁▁▁▁▇ |\n|anc_baby |Aggregated anchoring response, combining variables babieshigh and babieslow in one variable |numeric |NA |NA |1 |262 |263 |NA |NA |NA |NA |NA |77613.63 |4e+05 |1.8 |1000 |10000 |36750 |4e+06 |▇▁▁▁▁▁▁▁ |\n|anc_everest |Aggregated anchoring response, combining variables everesthigh and everestlow in one variable |numeric |NA |NA |1 |262 |263 |NA |NA |NA |NA |NA |8981.49 |8176.08 |8 |6967 |8500 |9000 |84000 |▇▁▁▁▁▁▁▁ |\n|anc_chicago |Aggregated anchoring response, combining variables chicagohigh and chicagolow in one variable |numeric |NA |NA |1 |262 |263 |NA |NA |NA |NA |NA |4.52 |6.27 |1e-04 |2 |3 |5 |50 |▇▁▁▁▁▁▁▁ |\n|gender_r |Gender variable cleaned for grammar, language variations and orthography |character |NA |NA |3 |260 |263 |0 |3 |NA |4 |10 |NA |NA |NA |NA |NA |NA |NA |NA |\n|oq |NA |character |NA |NA |33 |230 |263 |0 |227 |NA |2 |897 |NA |NA |NA |NA |NA |NA |NA |NA |\n|bf_1 |TIPI item 1, Extraversion: I see myself as extraverted, enthousiastic. |integer |1. Disagree strongly, - 2. Disagree moderately, - 3. Disagree a little, - 4. Neither agree nor disagree, - 5. Agree a little, - 6. Agree moderately, - 7. Agree strongly |NA |1 |262 |263 |NA |NA |NA |NA |NA |3.77 |1.79 |1 |2 |4 |5 |7 |▅▆▇▅▁▇▅▂ |\n|bf_2R |TIPI item 2, Agreeableness: I see myself as critical, quarrelsome. |numeric |1. Disagree strongly, - 2. Disagree moderately, - 3. Disagree a little, - 4. Neither agree nor disagree, - 5. Agree a little, - 6. Agree moderately, - 7. Agree strongly |NA |1 |262 |263 |NA |NA |NA |NA |NA |4.23 |1.61 |1 |3 |5 |5 |7 |▂▃▃▃▁▇▅▁ |\n|bf_3 |TIPI item 3, Conscientiousness: I see myself as dependable, self-disciplined. |integer |1. Disagree strongly, - 2. Disagree moderately, - 3. Disagree a little, - 4. Neither agree nor disagree, - 5. Agree a little, - 6. Agree moderately, - 7. Agree strongly |NA |1 |262 |263 |NA |NA |NA |NA |NA |5.03 |1.4 |1 |4 |5 |6 |7 |▁▁▂▃▁▇▇▃ |\n|bf_5R |TIPI item 4, Neuroticsm: I see myself as anxious, easily upset. |numeric |1. Disagree strongly, - 2. Disagree moderately, - 3. Disagree a little, - 4. Neither agree nor disagree, - 5. Agree a little, - 6. Agree moderately, - 7. Agree strongly |NA |1 |262 |263 |NA |NA |NA |NA |NA |3.98 |1.82 |1 |2 |4 |5 |7 |▂▇▆▃▁▇▅▃ |\n|bf_6 |TIPI item 5, Openness to experience: I see myself as open to new experiences, complex. |integer |1. Disagree strongly, - 2. Disagree moderately, - 3. Disagree a little, - 4. Neither agree nor disagree, - 5. Agree a little, - 6. Agree moderately, - 7. Agree strongly |NA |1 |262 |263 |NA |NA |NA |NA |NA |5.29 |1.22 |1 |5 |5 |6 |7 |▁▁▁▂▁▇▆▃ |\n|bf_7R |TIPI item 6, Extraversion: I see myself as reserved, quiet. |numeric |7. Disagree strongly, - 6. Disagree moderately, - 5. Disagree a little, - 4. Neither agree nor disagree, - 3. Agree a little, - 2. Agree moderately, - 1. Agree strongly |NA |1 |262 |263 |NA |NA |NA |NA |NA |3.38 |1.73 |1 |2 |3 |5 |7 |▅▆▇▂▁▅▃▂ |\n|bf_8 |TIPI item 7, Agreeableness: I see myself as sympathetic, warm. |integer |1. Disagree strongly, - 2. Disagree moderately, - 3. Disagree a little, - 4. Neither agree nor disagree, - 5. Agree a little, - 6. Agree moderately, - 7. Agree strongly |NA |1 |262 |263 |NA |NA |NA |NA |NA |5.32 |1.34 |1 |5 |6 |6 |7 |▁▁▁▂▁▇▇▃ |\n|bf_10R |TIPI item 8, Conscientiousness: I see myself as disorganized, careless. |numeric |7. Disagree strongly, - 6. Disagree moderately, - 5. Disagree a little, - 4. Neither agree nor disagree, - 3. Agree a little, - 2. Agree moderately, - 1. Agree strongly |NA |1 |262 |263 |NA |NA |NA |NA |NA |5.05 |1.59 |1 |4 |5 |6 |7 |▁▂▅▃▁▅▇▆ |\n|bf_11 |TIPI item 9, Neuroticsm: I see myself as calm, emotionally stable. |integer |1. Disagree strongly, - 2. Disagree moderately, - 3. Disagree a little, - 4. Neither agree nor disagree, - 5. Agree a little, - 6. Agree moderately, - 7. Agree strongly |NA |1 |262 |263 |NA |NA |NA |NA |NA |4.68 |1.54 |1 |4 |5 |6 |7 |▁▂▅▅▁▇▇▃ |\n|bf_12R |TIPI item 10, Openness to experience: I see myself as conventional, uncreative. |numeric |7. Disagree strongly, - 6. Disagree moderately, - 5. Disagree a little, - 4. Neither agree nor disagree, - 3. Agree a little, - 2. Agree moderately, - 1. Agree strongly |NA |1 |262 |263 |NA |NA |NA |NA |NA |4.79 |1.51 |1 |4 |5 |6 |7 |▁▂▃▅▁▆▇▃ |\n|soc_d_1 |Social desirability questionnaire item 1: Before voting I thoroughly investigate the qualifications of all the candidates. |integer |0. No, - 1. Yes |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.67 |0.47 |0 |0 |1 |1 |1 |▃▁▁▁▁▁▁▇ |\n|soc_d_2 |Social desirability questionnaire item 2: I never hesitate to go out of my way to help someone in trouble. |integer |0. No, - 1. Yes |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.68 |0.47 |0 |0 |1 |1 |1 |▃▁▁▁▁▁▁▇ |\n|soc_d_3R |Social desirability questionnaire item 3: It is sometimes hard for me to go on with my work if I am not encouraged. |numeric |0. No, - 1. Yes |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.65 |0.48 |0 |0 |1 |1 |1 |▅▁▁▁▁▁▁▇ |\n|soc_d_4 |Social desirability questionnaire item 4: I have never intensely disliked anyone. |integer |0. No, - 1. Yes |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.21 |0.41 |0 |0 |0 |0 |1 |▇▁▁▁▁▁▁▂ |\n|soc_d_5R |Social desirability questionnaire item 5: On occasion I have had doubts about my ability to succeed in life. |numeric |0. No, - 1. Yes |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.84 |0.37 |0 |1 |1 |1 |1 |▂▁▁▁▁▁▁▇ |\n|soc_d_6R |Social desirability questionnaire item 6: I sometimes feel resentful when I don't get my way. |numeric |0. No, - 1. Yes |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.7 |0.46 |0 |0 |1 |1 |1 |▃▁▁▁▁▁▁▇ |\n|soc_d_7 |Social desirability questionnaire item 7: I am always careful about my manner of dress. |integer |0. No, - 1. Yes |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.49 |0.5 |0 |0 |0 |1 |1 |▇▁▁▁▁▁▁▇ |\n|soc_d_8 |Social desirability questionnaire item 8: My table manners at home are as good as when I eat out in a restaurant. |integer |0. No, - 1. Yes |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.53 |0.5 |0 |0 |1 |1 |1 |▇▁▁▁▁▁▁▇ |\n|soc_d_9R |Social desirability questionnaire item 9: If I could get into a movie without paying and be sure I was not seen I would probably do it. |numeric |0. No, - 1. Yes |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.53 |0.5 |0 |0 |1 |1 |1 |▇▁▁▁▁▁▁▇ |\n|soc_d_10R |Social desirability questionnaire item 10: On a few occasions, I have given up doing something because I thought too little of my ability. |numeric |0. No, - 1. Yes |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.73 |0.45 |0 |0 |1 |1 |1 |▃▁▁▁▁▁▁▇ |\n|soc_d_11R |Social desirability questionnaire item 11: I like to gossip at times. |numeric |0. No, - 1. Yes |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.66 |0.47 |0 |0 |1 |1 |1 |▅▁▁▁▁▁▁▇ |\n|soc_d_12R |Social desirability questionnaire item 12: There have been times when I felt like rebelling against people in authority even though I knew they were right. |numeric |0. No, - 1. Yes |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.51 |0.5 |0 |0 |1 |1 |1 |▇▁▁▁▁▁▁▇ |\n|soc_d_13 |Social desirability questionnaire item 13: No matter who I'm talking to, I'm always a good listener. |integer |0. No, - 1. Yes |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.72 |0.45 |0 |0 |1 |1 |1 |▃▁▁▁▁▁▁▇ |\n|soc_d_14R |Social desirability questionnaire item 14: I can remember 'playing sick' to get out of something. |numeric |0. No, - 1. Yes |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.72 |0.45 |0 |0 |1 |1 |1 |▃▁▁▁▁▁▁▇ |\n|soc_d_15R |Social desirability questionnaire item 15: There have been occasions when I took advantage of someone. |numeric |0. No, - 1. Yes |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.58 |0.5 |0 |0 |1 |1 |1 |▆▁▁▁▁▁▁▇ |\n|soc_d_16 |Social desirability questionnaire item 16: I'm always willing to admit it when I make a mistake. |integer |0. No, - 1. Yes |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.63 |0.48 |0 |0 |1 |1 |1 |▅▁▁▁▁▁▁▇ |\n|soc_d_17 |Social desirability questionnaire item 17: I always try to practice what I preach. |integer |0. No, - 1. Yes |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.85 |0.35 |0 |1 |1 |1 |1 |▂▁▁▁▁▁▁▇ |\n|soc_d_18 |Social desirability questionnaire item 18: I don't find it particularly difficult to get along with loud mouthed, obnoxious people. |integer |0. No, - 1. Yes |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.26 |0.44 |0 |0 |0 |1 |1 |▇▁▁▁▁▁▁▃ |\n|soc_d_19R |Social desirability questionnaire item 19: I sometimes try to get even rather than forgive and forget. |numeric |0. No, - 1. Yes |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.56 |0.5 |0 |0 |1 |1 |1 |▆▁▁▁▁▁▁▇ |\n|soc_d_20 |Social desirability questionnaire item 20: When I don't know something I don't at all mind admitting it. |integer |0. No, - 1. Yes |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.79 |0.41 |0 |1 |1 |1 |1 |▂▁▁▁▁▁▁▇ |\n|soc_d_21 |Social desirability questionnaire item 21: I am always courteous, even to people who are disagreeable. |integer |0. No, - 1. Yes |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.63 |0.48 |0 |0 |1 |1 |1 |▅▁▁▁▁▁▁▇ |\n|soc_d_22R |Social desirability questionnaire item 22: At times I have really insisted on having things my own way. |numeric |0. No, - 1. Yes |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.8 |0.4 |0 |1 |1 |1 |1 |▂▁▁▁▁▁▁▇ |\n|soc_d_23R |Social desirability questionnaire item 23: There have been occasions when I felt like smashing things. |numeric |0. No, - 1. Yes |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.84 |0.36 |0 |1 |1 |1 |1 |▂▁▁▁▁▁▁▇ |\n|soc_d_24 |Social desirability questionnaire item 24: I would never think of letting someone else be punished for my wrong- doings. |integer |0. No, - 1. Yes |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.82 |0.38 |0 |1 |1 |1 |1 |▂▁▁▁▁▁▁▇ |\n|soc_d_25 |Social desirability questionnaire item 25: I never resent being asked to return a favor. |integer |0. No, - 1. Yes |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.72 |0.45 |0 |0 |1 |1 |1 |▃▁▁▁▁▁▁▇ |\n|soc_d_26 |Social desirability questionnaire item 26: I have never been irked when people expressed ideas very different from my own. |integer |0. No, - 1. Yes |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.4 |0.49 |0 |0 |0 |1 |1 |▇▁▁▁▁▁▁▅ |\n|soc_d_27 |Social desirability questionnaire item 27: I never make a long trip without checking the safety of my car. |integer |0. No, - 1. Yes |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.51 |0.5 |0 |0 |1 |1 |1 |▇▁▁▁▁▁▁▇ |\n|soc_d_28R |Social desirability questionnaire item 28: There have been times when I was quite jealous of the good fortune of others. |numeric |0. No, - 1. Yes |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.84 |0.36 |0 |1 |1 |1 |1 |▂▁▁▁▁▁▁▇ |\n|soc_d_29 |Social desirability questionnaire item 29: I have almost never felt the urge to tell someone off. |integer |0. No, - 1. Yes |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.27 |0.44 |0 |0 |0 |1 |1 |▇▁▁▁▁▁▁▃ |\n|soc_d_30R |Social desirability questionnaire item 30: I am sometimes irritated by people who ask favors of me. |numeric |0. No, - 1. Yes |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.58 |0.49 |0 |0 |1 |1 |1 |▆▁▁▁▁▁▁▇ |\n|soc_d_31 |Social desirability questionnaire item 31: I have never felt that I was punished without cause. |integer |0. No, - 1. Yes |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.26 |0.44 |0 |0 |0 |1 |1 |▇▁▁▁▁▁▁▃ |\n|soc_d_32R |Social desirability questionnaire item 32: I sometimes think when people have a misfortune they only got what they deserved. |numeric |0. No, - 1. Yes |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.47 |0.5 |0 |0 |0 |1 |1 |▇▁▁▁▁▁▁▇ |\n|soc_d_33 |Social desirability questionnaire item 33: I have never deliberately said something that hurt someone's feelings. |integer |0. No, - 1. Yes |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.3 |0.46 |0 |0 |0 |1 |1 |▇▁▁▁▁▁▁▃ |\n|cr_1R |Careless response item 1: I am using an electronic device at this moment. |numeric |1. Yes, - 0. No |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.91 |0.29 |0 |1 |1 |1 |1 |▁▁▁▁▁▁▁▇ |\n|cr_2 |Careless response item 2: I turn into a leprechaun at night. |integer |0. Yes, - 1. No |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.16 |0.37 |0 |0 |0 |0 |1 |▇▁▁▁▁▁▁▂ |\n|cr_3 |Careless response item 3: All my friends are aliens. |integer |0. Yes, - 1. No |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.031 |0.17 |0 |0 |0 |0 |1 |▇▁▁▁▁▁▁▁ |\n|cr_4 |Careless response item 4: All my friends say I would make a great poodle. |integer |0. Yes, - 1. No |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.08 |0.27 |0 |0 |0 |0 |1 |▇▁▁▁▁▁▁▁ |\n|cr_5 |Careless response item 5: I eat cement occasionally. |integer |0. Yes, - 1. No |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.011 |0.11 |0 |0 |0 |0 |1 |▇▁▁▁▁▁▁▁ |\n|cr_6 |Careless response item 6: I can teleport across time and space. |integer |0. Yes, - 1. No |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.0076 |0.087 |0 |0 |0 |0 |1 |▇▁▁▁▁▁▁▁ |\n|cr_7 |Careless response item 7: I will be punished for meeting the requirements of my job. |integer |0. Yes, - 1. No |NA |1 |262 |263 |NA |NA |NA |NA |NA |0.05 |0.22 |0 |0 |0 |0 |1 |▇▁▁▁▁▁▁▁ |\n|everesthigh |Anchoring paradigm, high anchor: Height of Mount Everest |numeric |NA |NA |137 |126 |263 |NA |NA |NA |NA |NA |11573.93 |9024.16 |8.85 |8650 |8900 |13337.5 |84000 |▇▃▁▁▁▁▁▁ |\n|chicagohigh |Anchoring paradigm, high anchor: Population of Chicago |numeric |NA |NA |137 |126 |263 |NA |NA |NA |NA |NA |313337.14 |1153850.61 |1.3 |3 |3 |7 |7e+06 |▇▁▁▁▁▁▁▁ |\n|babieshigh |Anchoring paradigm, high anchor: Babies born each day |numeric |NA |NA |137 |126 |263 |NA |NA |NA |NA |NA |110755.6 |455097.97 |1.8 |10000 |30000 |50000 |4e+06 |▇▁▁▁▁▁▁▁ |\n|everestlow |Anchoring paradigm, low anchor: Height of Mount Everest |numeric |NA |NA |127 |136 |263 |NA |NA |NA |NA |NA |6579.68 |6461.63 |8 |2000 |8000 |8533 |60000 |▇▇▁▁▁▁▁▁ |\n|chicagolow |Anchoring paradigm, low anchor: Population of Chicago |numeric |NA |NA |127 |136 |263 |NA |NA |NA |NA |NA |409740.82 |4297754.77 |0.1 |1.2 |2.6 |5.05 |5e+07 |▇▁▁▁▁▁▁▁ |\n|babieslow |Anchoring paradgim, low anchor: Babies born each day |integer |NA |NA |127 |136 |263 |NA |NA |NA |NA |NA |46908.57 |341493.94 |10 |230 |1000 |10000 |3853000 |▇▁▁▁▁▁▁▁ |\n|d1 |NOT USED control question: memory of consent, not used: Think back to the beginning of this study. Do you remember clicking through a consent form, and the information it contained? |character |NA |NA |0 |263 |263 |0 |3 |NA |2 |3 |NA |NA |NA |NA |NA |NA |NA |NA |\n|d2.sq001 |NOT USED Answer option to control question d2: 'Do you remember if the consent form dealt with making your anonymous data accessible to others on osf.io?' : Yes, I remember |character |NA |NA |0 |263 |263 |0 |3 |NA |2 |3 |NA |NA |NA |NA |NA |NA |NA |NA |\n|d2.sq002 |NOT USED Answer option to control question d2: 'Do you remember if the consent form dealt with making your anonymous data accessible to others on osf.io?' : No, I don't remember |character |NA |NA |0 |263 |263 |0 |3 |NA |2 |3 |NA |NA |NA |NA |NA |NA |NA |NA |\n|d3.sq001 |NOT USED Answer option to control question d2: 'Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?': Yes |character |NA |NA |0 |263 |263 |0 |3 |NA |2 |3 |NA |NA |NA |NA |NA |NA |NA |NA |\n|d3.sq002 |NOT USED Answer option to control question d2: 'Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?': No |character |NA |NA |0 |263 |263 |0 |3 |NA |2 |3 |NA |NA |NA |NA |NA |NA |NA |NA |\n|d3.sq003 |NOT USED Answer option to control question d2: 'Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?': I don't remember |character |NA |NA |0 |263 |263 |0 |3 |NA |2 |3 |NA |NA |NA |NA |NA |NA |NA |NA |\n|gender |Gender: open-entry self-report |character |NA |NA |2 |261 |263 |0 |11 |NA |1 |11 |NA |NA |NA |NA |NA |NA |NA |NA |\n|age |Age categories |character |NA |NA |2 |261 |263 |0 |8 |NA |13 |13 |NA |NA |NA |NA |NA |NA |NA |NA |\n|end |NA |character |NA |NA |237 |26 |263 |0 |26 |NA |1 |228 |NA |NA |NA |NA |NA |NA |NA |NA |\n|return |NA |logical |NA |NA |263 |0 |263 |NA |NA |263 |NA |NA |NaN |NA |NA |NA |NA |NA |NA |NA |\n|lastpage |Last page completed by the participant, page 12 and 13 are considered as full participation |integer |NA |NA |0 |263 |263 |NA |NA |NA |NA |NA |12.19 |0.39 |12 |12 |12 |12 |13 |▇▁▁▁▁▁▁▂ |\n|random |Randomly attributed study condition. 1 & 2 = not shared, 3 & 4 = shared, 1 & 3 = high anchor in anchoring paradigm, 2 & 4 = low anchor. |integer |NA |NA |0 |263 |263 |NA |NA |NA |NA |NA |1.52 |0.5 |1 |1 |2 |2 |2 |▇▁▁▁▁▁▁▇ |\n|cb |No data sharing policy consent presented. One participant clicked on 'I disagree' but contacted the first author by email to indicate that they had 'a bug' and was unable to complete the questionnaire. See manuscript for details |character |NA |NA |0 |263 |263 |0 |2 |NA |7 |10 |NA |NA |NA |NA |NA |NA |NA |NA |\n|ca |Data sharing policy presented |logical |NA |NA |263 |0 |263 |NA |NA |263 |NA |NA |NaN |NA |NA |NA |NA |NA |NA |NA |\n|mc_1 |comprehension question consent 1 (distractor): Will this survey take longer than 10 minutes? |character |NA |NA |1 |262 |263 |0 |3 |NA |2 |16 |NA |NA |NA |NA |NA |NA |NA |NA |\n|mc_2 |comprehension question consent 2 (distractor): Is your data anonymous? |character |NA |NA |1 |262 |263 |0 |2 |NA |2 |3 |NA |NA |NA |NA |NA |NA |NA |NA |\n|mc_3 |comprehension question/manipulation check: will your data be shared? correct answer depends on condition: Will your data be shared? |character |NA |NA |1 |262 |263 |0 |3 |NA |2 |16 |NA |NA |NA |NA |NA |NA |NA |NA |\n|mc_4 |comprehension question consent 3 (distractor): Can you stop your participation at any time? |character |NA |NA |1 |262 |263 |0 |3 |NA |2 |16 |NA |NA |NA |NA |NA |NA |NA |NA |\n|Extraversion |2 bf items aggregated by rowMeans |numeric |NA |bf_1, bf_7R |1 |262 |263 |NA |NA |NA |NA |NA |3.57 |1.56 |1 |2.5 |3.5 |4.88 |7 |▅▇▃▆▃▅▂▂ |\n|Agreeableness |2 bf items aggregated by rowMeans |numeric |NA |bf_2R, bf_8 |1 |262 |263 |NA |NA |NA |NA |NA |4.54 |1.13 |1 |4 |4.5 |5 |7 |▁▁▁▇▆▆▂▂ |\n|Conscientiousness |2 bf items aggregated by rowMeans |numeric |NA |bf_3, bf_10R |1 |262 |263 |NA |NA |NA |NA |NA |5.04 |1.25 |1.5 |4.5 |5 |6 |7 |▁▁▂▂▅▇▅▅ |\n|Neuroticism |2 bf items aggregated by rowMeans |numeric |NA |bf_5R, bf_11 |1 |262 |263 |NA |NA |NA |NA |NA |4.35 |1.48 |1 |3 |4.5 |5.5 |7 |▁▃▃▇▂▇▃▃ |\n|Openness to experience |2 bf items aggregated by rowMeans |numeric |NA |bf_6, bf_12R |1 |262 |263 |NA |NA |NA |NA |NA |5.04 |1.1 |1.5 |4.5 |5 |6 |7 |▁▁▂▂▃▇▃▃ |\n|Social Desirability |33 soc_d items aggregated by rowMeans |numeric |NA |soc_d_1, soc_d_2, soc_d_3R, soc_d_4, soc_d_5R, soc_d_6R, soc_d_7, soc_d_8, soc_d_9R, soc_d_10R, soc_d_11R, soc_d_12R, soc_d_13, soc_d_14R, soc_d_15R, soc_d_16, soc_d_17, soc_d_18, soc_d_19R, soc_d_20, soc_d_21, soc_d_22R, soc_d_23R, soc_d_24, soc_d_25, soc_d_26, soc_d_27, soc_d_28R, soc_d_29, soc_d_30R, soc_d_31, soc_d_32R, soc_d_33 |1 |262 |263 |NA |NA |NA |NA |NA |0.45 |0.14 |0.12 |0.33 |0.42 |0.55 |0.88 |▂▃▇▆▅▃▂▁ |\n|Careless responses |7 cr items aggregated by rowMeans |numeric |NA |cr_1R, cr_2, cr_3, cr_4, cr_5, cr_6, cr_7 |1 |262 |263 |NA |NA |NA |NA |NA |0.18 |0.094 |0 |0.14 |0.14 |0.14 |0.57 |▁▇▁▂▁▁▁▁ |\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.8.1).",
"identifier": "https://doi.org/10.17605/OSF.IO/AM6BC",
"creator": "Julia C. Eberlen, Emmanuel Nicaise, Sarah Leveaux, Youri L. Mora, Olivier Klein",
"citation": "Eberlen, J. C., Nicaise, E., Leveaux, S., Mora, Y., & Klein, O. (2019, August 5). Data collected online. https://doi.org/10.17605/OSF.IO/AM6BC",
"datePublished": "2019-08-06",
"temporalCoverage": "2019-06-17 to 2019-06-21",
"spatialCoverage": "Online participants residing in, or citizens of, the EU at time of data collection",
"keywords": ["V1", "id", "consent", "cond_anc", "refused", "remember", "anc_baby", "anc_everest", "anc_chicago", "gender_r", "oq", "bf_1", "bf_2R", "bf_3", "bf_5R", "bf_6", "bf_7R", "bf_8", "bf_10R", "bf_11", "bf_12R", "soc_d_1", "soc_d_2", "soc_d_3R", "soc_d_4", "soc_d_5R", "soc_d_6R", "soc_d_7", "soc_d_8", "soc_d_9R", "soc_d_10R", "soc_d_11R", "soc_d_12R", "soc_d_13", "soc_d_14R", "soc_d_15R", "soc_d_16", "soc_d_17", "soc_d_18", "soc_d_19R", "soc_d_20", "soc_d_21", "soc_d_22R", "soc_d_23R", "soc_d_24", "soc_d_25", "soc_d_26", "soc_d_27", "soc_d_28R", "soc_d_29", "soc_d_30R", "soc_d_31", "soc_d_32R", "soc_d_33", "cr_1R", "cr_2", "cr_3", "cr_4", "cr_5", "cr_6", "cr_7", "everesthigh", "chicagohigh", "babieshigh", "everestlow", "chicagolow", "babieslow", "d1", "d2.sq001", "d2.sq002", "d3.sq001", "d3.sq002", "d3.sq003", "gender", "age", "end", "return", "lastpage", "random", "cb", "ca", "mc_1", "mc_2", "mc_3", "mc_4", "Extraversion", "Agreeableness", "Conscientiousness", "Neuroticism", "Openness to experience", "Social Desirability", "Careless responses"],
"@context": "http://schema.org/",
"@type": "Dataset",
"variableMeasured": [
{
"name": "V1",
"@type": "propertyValue"
},
{
"name": "id",
"description": "ID variable from raw data",
"@type": "propertyValue"
},
{
"name": "consent",
"description": "Variables cb and ca combined in one variable",
"@type": "propertyValue"
},
{
"name": "cond_anc",
"description": "Anchoring condition: high and low",
"@type": "propertyValue"
},
{
"name": "refused",
"description": "Refusal to participate, one participant clicked on 'I disagree' but contacted the first author by email to indicate that they had 'a bug' and was unable to complete the questionnaire. This participant was in the 'no data sharing' condition.",
"@type": "propertyValue"
},
{
"name": "remember",
"description": "Mutated variable from consent and mc_3: Does the participant remember the correct data sharing policy?",
"value": "0. No or wrong memory,\n1. correct memory",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "anc_baby",
"description": "Aggregated anchoring response, combining variables babieshigh and babieslow in one variable",
"@type": "propertyValue"
},
{
"name": "anc_everest",
"description": "Aggregated anchoring response, combining variables everesthigh and everestlow in one variable",
"@type": "propertyValue"
},
{
"name": "anc_chicago",
"description": "Aggregated anchoring response, combining variables chicagohigh and chicagolow in one variable",
"@type": "propertyValue"
},
{
"name": "gender_r",
"description": "Gender variable cleaned for grammar, language variations and orthography",
"@type": "propertyValue"
},
{
"name": "oq",
"@type": "propertyValue"
},
{
"name": "bf_1",
"description": "TIPI item 1, Extraversion: I see myself as extraverted, enthousiastic.",
"value": "1. Disagree strongly,\n2. Disagree moderately,\n3. Disagree a little,\n4. Neither agree nor disagree,\n5. Agree a little,\n6. Agree moderately,\n7. Agree strongly",
"maxValue": 7,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "bf_2R",
"description": "TIPI item 2, Agreeableness: I see myself as critical, quarrelsome.",
"value": "1. Disagree strongly,\n2. Disagree moderately,\n3. Disagree a little,\n4. Neither agree nor disagree,\n5. Agree a little,\n6. Agree moderately,\n7. Agree strongly",
"maxValue": 7,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "bf_3",
"description": "TIPI item 3, Conscientiousness: I see myself as dependable, self-disciplined.",
"value": "1. Disagree strongly,\n2. Disagree moderately,\n3. Disagree a little,\n4. Neither agree nor disagree,\n5. Agree a little,\n6. Agree moderately,\n7. Agree strongly",
"maxValue": 7,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "bf_5R",
"description": "TIPI item 4, Neuroticsm: I see myself as anxious, easily upset.",
"value": "1. Disagree strongly,\n2. Disagree moderately,\n3. Disagree a little,\n4. Neither agree nor disagree,\n5. Agree a little,\n6. Agree moderately,\n7. Agree strongly",
"maxValue": 7,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "bf_6",
"description": "TIPI item 5, Openness to experience: I see myself as open to new experiences, complex.",
"value": "1. Disagree strongly,\n2. Disagree moderately,\n3. Disagree a little,\n4. Neither agree nor disagree,\n5. Agree a little,\n6. Agree moderately,\n7. Agree strongly",
"maxValue": 7,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "bf_7R",
"description": "TIPI item 6, Extraversion: I see myself as reserved, quiet.",
"value": "7. Disagree strongly,\n6. Disagree moderately,\n5. Disagree a little,\n4. Neither agree nor disagree,\n3. Agree a little,\n2. Agree moderately,\n1. Agree strongly",
"maxValue": 7,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "bf_8",
"description": "TIPI item 7, Agreeableness: I see myself as sympathetic, warm.",
"value": "1. Disagree strongly,\n2. Disagree moderately,\n3. Disagree a little,\n4. Neither agree nor disagree,\n5. Agree a little,\n6. Agree moderately,\n7. Agree strongly",
"maxValue": 7,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "bf_10R",
"description": "TIPI item 8, Conscientiousness: I see myself as disorganized, careless.",
"value": "7. Disagree strongly,\n6. Disagree moderately,\n5. Disagree a little,\n4. Neither agree nor disagree,\n3. Agree a little,\n2. Agree moderately,\n1. Agree strongly",
"maxValue": 7,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "bf_11",
"description": "TIPI item 9, Neuroticsm: I see myself as calm, emotionally stable.",
"value": "1. Disagree strongly,\n2. Disagree moderately,\n3. Disagree a little,\n4. Neither agree nor disagree,\n5. Agree a little,\n6. Agree moderately,\n7. Agree strongly",
"maxValue": 7,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "bf_12R",
"description": "TIPI item 10, Openness to experience: I see myself as conventional, uncreative.",
"value": "7. Disagree strongly,\n6. Disagree moderately,\n5. Disagree a little,\n4. Neither agree nor disagree,\n3. Agree a little,\n2. Agree moderately,\n1. Agree strongly",
"maxValue": 7,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "soc_d_1",
"description": "Social desirability questionnaire item 1: Before voting I thoroughly investigate the qualifications of all the candidates.",
"value": "0. No,\n1. Yes",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "soc_d_2",
"description": "Social desirability questionnaire item 2: I never hesitate to go out of my way to help someone in trouble.",
"value": "0. No,\n1. Yes",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "soc_d_3R",
"description": "Social desirability questionnaire item 3: It is sometimes hard for me to go on with my work if I am not encouraged.",
"value": "0. No,\n1. Yes",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "soc_d_4",
"description": "Social desirability questionnaire item 4: I have never intensely disliked anyone.",
"value": "0. No,\n1. Yes",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "soc_d_5R",
"description": "Social desirability questionnaire item 5: On occasion I have had doubts about my ability to succeed in life.",
"value": "0. No,\n1. Yes",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "soc_d_6R",
"description": "Social desirability questionnaire item 6: I sometimes feel resentful when I don't get my way.",
"value": "0. No,\n1. Yes",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "soc_d_7",
"description": "Social desirability questionnaire item 7: I am always careful about my manner of dress.",
"value": "0. No,\n1. Yes",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "soc_d_8",
"description": "Social desirability questionnaire item 8: My table manners at home are as good as when I eat out in a restaurant.",
"value": "0. No,\n1. Yes",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "soc_d_9R",
"description": "Social desirability questionnaire item 9: If I could get into a movie without paying and be sure I was not seen I would probably do it.",
"value": "0. No,\n1. Yes",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "soc_d_10R",
"description": "Social desirability questionnaire item 10: On a few occasions, I have given up doing something because I thought too little of my ability.",
"value": "0. No,\n1. Yes",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "soc_d_11R",
"description": "Social desirability questionnaire item 11: I like to gossip at times.",
"value": "0. No,\n1. Yes",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "soc_d_12R",
"description": "Social desirability questionnaire item 12: There have been times when I felt like rebelling against people in authority even though I knew they were right.",
"value": "0. No,\n1. Yes",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "soc_d_13",
"description": "Social desirability questionnaire item 13: No matter who I'm talking to, I'm always a good listener.",
"value": "0. No,\n1. Yes",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "soc_d_14R",
"description": "Social desirability questionnaire item 14: I can remember 'playing sick' to get out of something.",
"value": "0. No,\n1. Yes",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "soc_d_15R",
"description": "Social desirability questionnaire item 15: There have been occasions when I took advantage of someone.",
"value": "0. No,\n1. Yes",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "soc_d_16",
"description": "Social desirability questionnaire item 16: I'm always willing to admit it when I make a mistake.",
"value": "0. No,\n1. Yes",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "soc_d_17",
"description": "Social desirability questionnaire item 17: I always try to practice what I preach.",
"value": "0. No,\n1. Yes",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "soc_d_18",
"description": "Social desirability questionnaire item 18: I don't find it particularly difficult to get along with loud mouthed, obnoxious people.",
"value": "0. No,\n1. Yes",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "soc_d_19R",
"description": "Social desirability questionnaire item 19: I sometimes try to get even rather than forgive and forget.",
"value": "0. No,\n1. Yes",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "soc_d_20",
"description": "Social desirability questionnaire item 20: When I don't know something I don't at all mind admitting it.",
"value": "0. No,\n1. Yes",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "soc_d_21",
"description": "Social desirability questionnaire item 21: I am always courteous, even to people who are disagreeable.",
"value": "0. No,\n1. Yes",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "soc_d_22R",
"description": "Social desirability questionnaire item 22: At times I have really insisted on having things my own way.",
"value": "0. No,\n1. Yes",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "soc_d_23R",
"description": "Social desirability questionnaire item 23: There have been occasions when I felt like smashing things.",
"value": "0. No,\n1. Yes",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "soc_d_24",
"description": "Social desirability questionnaire item 24: I would never think of letting someone else be punished for my wrong- doings.",
"value": "0. No,\n1. Yes",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "soc_d_25",
"description": "Social desirability questionnaire item 25: I never resent being asked to return a favor.",
"value": "0. No,\n1. Yes",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "soc_d_26",
"description": "Social desirability questionnaire item 26: I have never been irked when people expressed ideas very different from my own.",
"value": "0. No,\n1. Yes",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "soc_d_27",
"description": "Social desirability questionnaire item 27: I never make a long trip without checking the safety of my car.",
"value": "0. No,\n1. Yes",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "soc_d_28R",
"description": "Social desirability questionnaire item 28: There have been times when I was quite jealous of the good fortune of others.",
"value": "0. No,\n1. Yes",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "soc_d_29",
"description": "Social desirability questionnaire item 29: I have almost never felt the urge to tell someone off.",
"value": "0. No,\n1. Yes",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "soc_d_30R",
"description": "Social desirability questionnaire item 30: I am sometimes irritated by people who ask favors of me. ",
"value": "0. No,\n1. Yes",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "soc_d_31",
"description": "Social desirability questionnaire item 31: I have never felt that I was punished without cause.",
"value": "0. No,\n1. Yes",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "soc_d_32R",
"description": "Social desirability questionnaire item 32: I sometimes think when people have a misfortune they only got what they deserved.",
"value": "0. No,\n1. Yes",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "soc_d_33",
"description": "Social desirability questionnaire item 33: I have never deliberately said something that hurt someone's feelings.",
"value": "0. No,\n1. Yes",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "cr_1R",
"description": "Careless response item 1: I am using an electronic device at this moment.",
"value": "1. Yes,\n0. No",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "cr_2",
"description": "Careless response item 2: I turn into a leprechaun at night.",
"value": "0. Yes,\n1. No",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "cr_3",
"description": "Careless response item 3: All my friends are aliens.",
"value": "0. Yes,\n1. No",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "cr_4",
"description": "Careless response item 4: All my friends say I would make a great poodle.",
"value": "0. Yes,\n1. No",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "cr_5",
"description": "Careless response item 5: I eat cement occasionally.",
"value": "0. Yes,\n1. No",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "cr_6",
"description": "Careless response item 6: I can teleport across time and space.",
"value": "0. Yes,\n1. No",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "cr_7",
"description": "Careless response item 7: I will be punished for meeting the requirements of my job.",
"value": "0. Yes,\n1. No",
"maxValue": 1,
"minValue": 0,
"@type": "propertyValue"
},
{
"name": "everesthigh",
"description": "Anchoring paradigm, high anchor: Height of Mount Everest",
"@type": "propertyValue"
},
{
"name": "chicagohigh",
"description": "Anchoring paradigm, high anchor: Population of Chicago",
"@type": "propertyValue"
},
{
"name": "babieshigh",
"description": "Anchoring paradigm, high anchor: Babies born each day",
"@type": "propertyValue"
},
{
"name": "everestlow",
"description": "Anchoring paradigm, low anchor: Height of Mount Everest",
"@type": "propertyValue"
},
{
"name": "chicagolow",
"description": "Anchoring paradigm, low anchor: Population of Chicago",
"@type": "propertyValue"
},
{
"name": "babieslow",
"description": "Anchoring paradgim, low anchor: Babies born each day",
"@type": "propertyValue"
},
{
"name": "d1",
"description": "NOT USED control question: memory of consent, not used: Think back to the beginning of this study. Do you remember clicking through a consent form, and the information it contained?",
"@type": "propertyValue"
},
{
"name": "d2.sq001",
"description": "NOT USED Answer option to control question d2: 'Do you remember if the consent form dealt with making your anonymous data accessible to others on osf.io?' : Yes, I remember",
"@type": "propertyValue"
},
{
"name": "d2.sq002",
"description": "NOT USED Answer option to control question d2: 'Do you remember if the consent form dealt with making your anonymous data accessible to others on osf.io?' : No, I don't remember",
"@type": "propertyValue"
},
{
"name": "d3.sq001",
"description": "NOT USED Answer option to control question d2: 'Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?': Yes\n",
"@type": "propertyValue"
},
{
"name": "d3.sq002",
"description": "NOT USED Answer option to control question d2: 'Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?': No",
"@type": "propertyValue"
},
{
"name": "d3.sq003",
"description": "NOT USED Answer option to control question d2: 'Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?': I don't remember",
"@type": "propertyValue"
},
{
"name": "gender",
"description": "Gender: open-entry self-report",
"@type": "propertyValue"
},
{
"name": "age",
"description": "Age categories",
"@type": "propertyValue"
},
{
"name": "end",
"@type": "propertyValue"
},
{
"name": "return",
"@type": "propertyValue"
},
{
"name": "lastpage",
"description": "Last page completed by the participant, page 12 and 13 are considered as full participation",
"@type": "propertyValue"
},
{
"name": "random",
"description": "Randomly attributed study condition. 1 & 2 = not shared, 3 & 4 = shared, 1 & 3 = high anchor in anchoring paradigm, 2 & 4 = low anchor.",
"@type": "propertyValue"
},
{
"name": "cb",
"description": "No data sharing policy consent presented. One participant clicked on 'I disagree' but contacted the first author by email to indicate that they had 'a bug' and was unable to complete the questionnaire. See manuscript for details",
"@type": "propertyValue"
},
{
"name": "ca",
"description": "Data sharing policy presented",
"@type": "propertyValue"
},
{
"name": "mc_1",
"description": "comprehension question consent 1 (distractor): Will this survey take longer than 10 minutes?",
"@type": "propertyValue"
},
{
"name": "mc_2",
"description": "comprehension question consent 2 (distractor): Is your data anonymous?",
"@type": "propertyValue"
},
{
"name": "mc_3",
"description": "comprehension question/manipulation check: will your data be shared? correct answer depends on condition: Will your data be shared?",
"@type": "propertyValue"
},
{
"name": "mc_4",
"description": "comprehension question consent 3 (distractor): Can you stop your participation at any time?",
"@type": "propertyValue"
},
{
"name": "Extraversion",
"description": "2 bf items aggregated by rowMeans",
"@type": "propertyValue"
},
{
"name": "Agreeableness",
"description": "2 bf items aggregated by rowMeans",
"@type": "propertyValue"
},
{
"name": "Conscientiousness",
"description": "2 bf items aggregated by rowMeans",
"@type": "propertyValue"
},
{
"name": "Neuroticism",
"description": "2 bf items aggregated by rowMeans",
"@type": "propertyValue"
},
{
"name": "Openness to experience",
"description": "2 bf items aggregated by rowMeans",
"@type": "propertyValue"
},
{
"name": "Social Desirability",
"description": "33 soc_d items aggregated by rowMeans",
"@type": "propertyValue"
},
{
"name": "Careless responses",
"description": "7 cr items aggregated by rowMeans",
"@type": "propertyValue"
}
]
}`